Phase 5B commit 4: delete style/shape/voice-matching/model-routing systems
Backend cleanup: - Delete STYLE_SPEED_MODIFIERS, STYLE_PHONE_QUALITY constants - Delete _normalize_style_key, _match_voices_to_styles, get_style_speed_modifier, get_style_phone_quality functions - Delete _pick_caller_style, _HEAVY_STYLES, _LIGHT_STYLES, _EVASIVE_STYLES (only called by template path) - Delete _is_informal_style + _INFORMAL_STYLES - Remove orphaned calls to deleted _assign_call_shape - Drop shape parameter from _pick_response_budget - Simplify _assess_call_quality (no more shape/style/pool_name) - Delete Session fields: caller_styles, caller_shapes, caller_model_strategy/pool/map/fallback/models/_cycle_idx - Remove those fields from Session.reset, checkpoint save/load, and session.caller property - Drop style/shape lookups in chat callsites and promotion gate - Update config.py caller_dialog model stale comment
This commit is contained in:
+1
-2
@@ -34,9 +34,8 @@ class Settings(BaseSettings):
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ollama_host: str = "http://localhost:11434"
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ollama_host: str = "http://localhost:11434"
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# Per-category model routing
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# Per-category model routing
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# caller_dialog is overridden by style_matched routing (see Session.caller_model_map)
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category_models: dict = {
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category_models: dict = {
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"caller_dialog": "x-ai/grok-4.1-fast", # fallback if style_matched disabled ($0.20/$0.50)
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"caller_dialog": "anthropic/claude-haiku-4.5", # live caller dialog ($0.80/$4)
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"devon_ask": "x-ai/grok-4.1-fast", # Devon matches show energy, cheap ($0.20/$0.50)
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"devon_ask": "x-ai/grok-4.1-fast", # Devon matches show energy, cheap ($0.20/$0.50)
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"devon_monitor": "google/gemini-2.5-flash", # just yes/no decisions, keep cheap ($0.15/$0.60)
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"devon_monitor": "google/gemini-2.5-flash", # just yes/no decisions, keep cheap ($0.15/$0.60)
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"background_gen": "anthropic/claude-sonnet-4.6", # backgrounds drive the whole call — worth the quality ($3/$15, ~$0.30/show)
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"background_gen": "anthropic/claude-sonnet-4.6", # backgrounds drive the whole call — worth the quality ($3/$15, ~$0.30/show)
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+14
-556
@@ -4994,101 +4994,6 @@ CALLER_STYLE_KEYS = [
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"rambling", # 17
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"rambling", # 17
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]
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]
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# Preferred voice dimensions for each communication style.
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# None = no preference (matcher can pick any value for that dimension).
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# Maps style key → dict of preferred VOICE_PROFILES dimensions.
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# Used by voice matching (Phase 2c) to score voices against caller personality.
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STYLE_VOICE_PREFERENCES = {
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"quiet_nervous": {"weight": "light", "energy": "low", "warmth": None, "age_feel": None},
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"storyteller": {"weight": "medium", "energy": "medium", "warmth": "warm", "age_feel": None},
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"deadpan": {"weight": "heavy", "energy": "low", "warmth": "cool", "age_feel": None},
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"high_energy": {"weight": None, "energy": "high", "warmth": "warm", "age_feel": "young"},
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"confrontational": {"weight": "heavy", "energy": "high", "warmth": "cool", "age_feel": None},
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"oversharer": {"weight": "medium", "energy": "medium", "warmth": "warm", "age_feel": None},
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"philosopher": {"weight": "heavy", "energy": "low", "warmth": "warm", "age_feel": "mature"},
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"bragger": {"weight": "heavy", "energy": "high", "warmth": "neutral", "age_feel": "middle"},
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"first_time": {"weight": "light", "energy": "low", "warmth": "warm", "age_feel": "young"},
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"emotional": {"weight": "medium", "energy": "low", "warmth": "warm", "age_feel": None},
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"world_weary": {"weight": "heavy", "energy": "low", "warmth": "cool", "age_feel": "mature"},
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"conspiracy": {"weight": "medium", "energy": "medium", "warmth": "neutral", "age_feel": "middle"},
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"comedian": {"weight": "medium", "energy": "high", "warmth": "warm", "age_feel": None},
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"angry_venting": {"weight": "heavy", "energy": "high", "warmth": "neutral", "age_feel": None},
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"sweet_earnest": {"weight": "light", "energy": "medium", "warmth": "warm", "age_feel": None},
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"mysterious": {"weight": "heavy", "energy": "low", "warmth": "cool", "age_feel": "middle"},
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"know_it_all": {"weight": "medium", "energy": "medium", "warmth": "cool", "age_feel": "middle"},
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"rambling": {"weight": "light", "energy": "high", "warmth": "warm", "age_feel": None},
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}
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# --- Call Shapes ---
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# Each shape defines the dramatic arc of a call. Weights control frequency.
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CALL_SHAPES = [
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("standard", 25), # Normal call — caller has a thing, they talk about it
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("escalating_reveal", 15), # Starts mundane, each exchange reveals something bigger
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("am_i_the_asshole", 10), # Caller wants validation but the situation is morally gray
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("confrontation", 10), # Caller is fired up and wants to argue/vent
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("celebration", 8), # Something great happened — caller is riding high
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("quick_hit", 10), # Short and punchy — one thing to say, says it, done
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("bait_and_switch", 8), # Starts as one thing, turns out to be something completely different
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("the_hangup", 7), # Caller gets upset/embarrassed and hangs up mid-call
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("reactive", 7), # Caller is reacting to something that happened earlier on the show
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]
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_CALL_SHAPE_NAMES = [s[0] for s in CALL_SHAPES]
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_CALL_SHAPE_WEIGHTS = [s[1] for s in CALL_SHAPES]
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# Shape-style affinities: multipliers for base shape weights per communication style
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SHAPE_STYLE_AFFINITIES = {
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"quiet_nervous": {"the_hangup": 2.0, "escalating_reveal": 1.5, "bait_and_switch": 1.5, "confrontation": 0.3},
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"storyteller": {"escalating_reveal": 2.0, "bait_and_switch": 1.5, "standard": 1.5, "quick_hit": 0.3},
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"deadpan": {"quick_hit": 1.5, "am_i_the_asshole": 1.5, "confrontation": 1.3},
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"high_energy": {"confrontation": 1.5, "celebration": 1.5, "reactive": 1.5, "the_hangup": 0.5},
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"confrontational": {"confrontation": 3.0, "reactive": 2.0, "am_i_the_asshole": 1.5, "celebration": 0.3},
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"oversharer": {"am_i_the_asshole": 2.0, "escalating_reveal": 1.5, "standard": 1.5},
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"philosopher": {"standard": 1.5, "reactive": 1.5, "confrontation": 1.3},
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"bragger": {"am_i_the_asshole": 2.0, "confrontation": 1.5, "celebration": 1.5, "the_hangup": 0.3},
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"first_time": {"standard": 2.0, "the_hangup": 1.5, "quick_hit": 0.5},
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"emotional": {"escalating_reveal": 2.0, "the_hangup": 1.5, "bait_and_switch": 1.5, "quick_hit": 0.3},
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"world_weary": {"standard": 1.5, "reactive": 1.5, "am_i_the_asshole": 1.3, "celebration": 0.3},
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"conspiracy": {"escalating_reveal": 2.0, "bait_and_switch": 1.5, "confrontation": 1.3},
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"comedian": {"quick_hit": 2.0, "bait_and_switch": 1.5, "celebration": 1.3, "the_hangup": 0.3},
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"angry_venting": {"confrontation": 2.5, "reactive": 2.0, "the_hangup": 1.5, "celebration": 0.2},
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"sweet_earnest": {"celebration": 2.0, "standard": 1.5, "reactive": 1.3, "confrontation": 0.3},
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"mysterious": {"the_hangup": 2.5, "escalating_reveal": 2.0, "bait_and_switch": 1.5, "quick_hit": 0.3},
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"know_it_all": {"confrontation": 1.5, "am_i_the_asshole": 1.5, "reactive": 1.3},
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"rambling": {"bait_and_switch": 1.5, "escalating_reveal": 1.5, "standard": 1.3, "quick_hit": 0.3},
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}
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def _pick_call_shape(style: str = "") -> str:
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"""Pick a call shape using weighted random selection.
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If a communication style is provided, applies affinity multipliers.
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Also avoids repeating the last used shape."""
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weights = list(_CALL_SHAPE_WEIGHTS)
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# Apply style affinities
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if style:
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style_key = _normalize_style_key(style)
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affinities = SHAPE_STYLE_AFFINITIES.get(style_key, {})
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for i, name in enumerate(_CALL_SHAPE_NAMES):
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if name in affinities:
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weights[i] *= affinities[name]
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# Reduce weight of recently used shapes to avoid consecutive repeats
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if hasattr(session, 'call_history') and session.call_history:
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# Check if any recent call used this shape
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recent_shapes = set()
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for record in session.call_history[-2:]:
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for k, v in session.caller_shapes.items():
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if CALLER_BASES.get(k, {}).get("name") == record.caller_name:
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recent_shapes.add(v)
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for i, name in enumerate(_CALL_SHAPE_NAMES):
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if name in recent_shapes:
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weights[i] *= 0.4 # Reduce but don't eliminate
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return random.choices(_CALL_SHAPE_NAMES, weights=weights, k=1)[0]
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def pick_location() -> str:
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def pick_location() -> str:
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if random.random() < 0.8:
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if random.random() < 0.8:
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@@ -5245,14 +5150,6 @@ Output ONLY valid JSON, no markdown fences."""
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person1, person2 = rng.sample(people_pool, 2)
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person1, person2 = rng.sample(people_pool, 2)
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tic1, tic2 = rng.sample(VERBAL_TICS, 2)
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tic1, tic2 = rng.sample(VERBAL_TICS, 2)
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# Restore stored communication style
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stored_style = seeds.get("style", "")
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if stored_style:
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for key, b in CALLER_BASES.items():
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if b is base or b.get("name") == base.get("name"):
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session.caller_styles[key] = stored_style
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break
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time_ctx = _get_time_context()
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time_ctx = _get_time_context()
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trait_str = ", ".join(traits) if traits else "a regular caller"
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trait_str = ", ".join(traits) if traits else "a regular caller"
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@@ -5460,72 +5357,6 @@ def _pick_unique_reason() -> tuple[str, str]:
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return reason, chosen
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return reason, chosen
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# Style indices by name fragment for filtering
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_HEAVY_STYLES = ["emotional", "raw", "quiet", "nervous", "world-weary", "sweet", "earnest"]
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_LIGHT_STYLES = ["comedian", "bragger", "high-energy", "confrontational"]
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_EVASIVE_STYLES = ["mysterious", "evasive"]
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def _pick_caller_style(reason: str, pool_name: str) -> str:
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"""Pick a communication style appropriate for the caller's reason and pool."""
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reason_lower = reason.lower()
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style_lower_map = [(s, s.lower()) for s in CALLER_STYLES]
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# Heavy emotional content — exclude styles that trivialize it
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heavy_keywords = ["dying", "suicide", "terminal", "cancer", "funeral", "dead ",
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"death", "grief", "miscarriage", "abuse", "assault", "murder"]
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if any(kw in reason_lower for kw in heavy_keywords):
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filtered = [s for s, sl in style_lower_map
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if not any(t in sl for t in _LIGHT_STYLES)]
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if filtered:
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return random.choice(filtered)
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# Gossip pool — evasive or oversharer fit well, exclude emotional/raw
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if pool_name == "GOSSIP":
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filtered = [s for s, sl in style_lower_map
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if not any(t in sl for t in _HEAVY_STYLES)]
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if filtered:
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return random.choice(filtered)
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# Stories pool — storyteller and high-energy fit, exclude evasive
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if pool_name == "STORIES":
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filtered = [s for s, sl in style_lower_map
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if not any(t in sl for t in _EVASIVE_STYLES)]
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if filtered:
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return random.choice(filtered)
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# Hot takes — confrontational/opinionated styles only
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if pool_name == "HOT_TAKES":
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_hot_take_exclude = ["quiet", "nervous", "sweet", "earnest", "emotional", "raw", "world-weary"]
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filtered = [s for s, sl in style_lower_map
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if not any(t in sl for t in _hot_take_exclude)]
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if filtered:
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return random.choice(filtered)
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# Topic/trivia calls — exclude emotional/raw styles
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if pool_name == "TOPIC_CALLIN":
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filtered = [s for s, sl in style_lower_map
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if not any(t in sl for t in ["emotional", "raw"])]
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if filtered:
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return random.choice(filtered)
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return random.choice(CALLER_STYLES)
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def _assign_call_shape(base: dict) -> str:
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"""Pick and store a call shape for a caller, logging the assignment.
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Uses style-based affinities when a communication style is assigned."""
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caller_key = None
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for key, b in CALLER_BASES.items():
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if b is base or b.get("name") == base.get("name"):
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caller_key = key
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break
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style = session.caller_styles.get(caller_key, "") if caller_key else ""
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shape = _pick_call_shape(style)
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if caller_key:
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session.caller_shapes[caller_key] = shape
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print(f"[Shape] {base.get('name', caller_key)} assigned shape: {shape} (style: {style[:30]})")
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return shape
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def generate_caller_background(base: dict) -> CallerBackground | str:
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def generate_caller_background(base: dict) -> CallerBackground | str:
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"""Generate a template-based background as fallback. The preferred path is
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"""Generate a template-based background as fallback. The preferred path is
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@@ -5550,16 +5381,6 @@ def generate_caller_background(base: dict) -> CallerBackground | str:
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# Core identity (problem or topic)
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# Core identity (problem or topic)
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reason, pool_name = _pick_unique_reason()
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reason, pool_name = _pick_unique_reason()
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# Assign communication style matched to content
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style = _pick_caller_style(reason, pool_name)
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for key, b in CALLER_BASES.items():
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if b is base or b.get("name") == base.get("name"):
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session.caller_styles[key] = style
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break
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# Assign call shape
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_assign_call_shape(base)
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interest1, interest2 = random.sample(INTERESTS, 2)
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interest1, interest2 = random.sample(INTERESTS, 2)
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quirk1, quirk2 = random.sample(QUIRKS, 2)
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quirk1, quirk2 = random.sample(QUIRKS, 2)
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@@ -5742,30 +5563,8 @@ async def _generate_caller_background_llm(base: dict) -> CallerBackground | str:
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else:
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else:
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reason, pool_name = _pick_unique_reason()
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reason, pool_name = _pick_unique_reason()
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# Assign communication style matched to content
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style_hint = ""
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style = _pick_caller_style(reason, pool_name)
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energy_level = random.choice(["medium", "high"])
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caller_key = None
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for key, b in CALLER_BASES.items():
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if b is base or b.get("name") == base.get("name"):
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caller_key = key
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break
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if caller_key:
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session.caller_styles[caller_key] = style
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style_hint = style.split(":")[1].strip()[:120] if ":" in style else ""
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# Determine energy level from style
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_high_energy_styles = {"high-energy", "confrontational", "angry/venting", "bragger", "comedian"}
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_low_energy_styles = {"quiet/nervous", "world-weary", "mysterious/evasive", "sweet/earnest", "emotional/raw"}
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style_label = style.split(":")[0].strip().lower() if ":" in style else style.lower()
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if style_label in _high_energy_styles:
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energy_level = random.choice(["high", "very_high"])
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elif style_label in _low_energy_styles:
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energy_level = random.choice(["low", "medium"])
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else:
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energy_level = random.choice(["medium", "high"])
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# Assign call shape
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_assign_call_shape(base)
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# Pick a few random color details as seeds — not a full list
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# Pick a few random color details as seeds — not a full list
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seeds = []
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seeds = []
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@@ -5923,106 +5722,9 @@ async def _regenerate_backgrounds_for_keys(keys: list[str]):
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session.caller_backgrounds[key] = generate_caller_background(CALLER_BASES[key])
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session.caller_backgrounds[key] = generate_caller_background(CALLER_BASES[key])
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else:
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else:
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session.caller_backgrounds[key] = result
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session.caller_backgrounds[key] = result
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# Clear cached model assignments so style matching re-evaluates
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for key in keys:
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session.caller_models.pop(key, None)
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# Re-run voice matching and queue sorting
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_match_voices_to_styles()
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_sort_caller_queue()
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print(f"[Background] Regenerated {len(keys)} caller backgrounds after theme change")
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print(f"[Background] Regenerated {len(keys)} caller backgrounds after theme change")
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# Dramatic shapes that play better later in the show
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_LATE_SHOW_SHAPES = {"escalating_reveal", "bait_and_switch", "the_hangup"}
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def _sort_caller_queue():
|
|
||||||
"""Sort caller presentation order for good show pacing.
|
|
||||||
Does NOT change which callers exist — only the order they're presented.
|
|
||||||
Prioritizes: energy alternation, topic variety, shape variety,
|
|
||||||
dramatic shapes later in the show."""
|
|
||||||
keys = list(session.caller_backgrounds.keys())
|
|
||||||
if not keys:
|
|
||||||
return
|
|
||||||
|
|
||||||
# Gather attributes for each caller
|
|
||||||
caller_attrs = {}
|
|
||||||
for key in keys:
|
|
||||||
bg = session.caller_backgrounds.get(key)
|
|
||||||
if isinstance(bg, CallerBackground):
|
|
||||||
energy = bg.energy_level
|
|
||||||
pool = bg.pool_name
|
|
||||||
else:
|
|
||||||
energy = "medium"
|
|
||||||
pool = ""
|
|
||||||
shape = session.caller_shapes.get(key, "standard")
|
|
||||||
caller_attrs[key] = {"energy": energy, "pool": pool, "shape": shape}
|
|
||||||
|
|
||||||
# Greedy placement: pick the best next caller at each position
|
|
||||||
remaining = list(keys)
|
|
||||||
ordered = []
|
|
||||||
|
|
||||||
for position in range(len(keys)):
|
|
||||||
best_key = None
|
|
||||||
best_score = -999
|
|
||||||
|
|
||||||
for key in remaining:
|
|
||||||
attrs = caller_attrs[key]
|
|
||||||
score = 0.0
|
|
||||||
|
|
||||||
# Energy alternation: penalize same energy as previous caller
|
|
||||||
if ordered:
|
|
||||||
prev_energy = caller_attrs[ordered[-1]]["energy"]
|
|
||||||
if attrs["energy"] == prev_energy:
|
|
||||||
score -= 3.0
|
|
||||||
# Bonus for contrast
|
|
||||||
high = {"high", "very_high"}
|
|
||||||
low = {"low", "medium"}
|
|
||||||
if (attrs["energy"] in high and prev_energy in low) or \
|
|
||||||
(attrs["energy"] in low and prev_energy in high):
|
|
||||||
score += 2.0
|
|
||||||
|
|
||||||
# Topic variety: penalize same pool as previous caller
|
|
||||||
if ordered:
|
|
||||||
prev_pool = caller_attrs[ordered[-1]]["pool"]
|
|
||||||
if attrs["pool"] and attrs["pool"] == prev_pool:
|
|
||||||
score -= 3.0
|
|
||||||
# Also check 2-back
|
|
||||||
if len(ordered) >= 2:
|
|
||||||
prev2_pool = caller_attrs[ordered[-2]]["pool"]
|
|
||||||
if attrs["pool"] and attrs["pool"] == prev2_pool:
|
|
||||||
score -= 1.5
|
|
||||||
|
|
||||||
# Shape variety: penalize same shape as previous caller
|
|
||||||
if ordered:
|
|
||||||
prev_shape = caller_attrs[ordered[-1]]["shape"]
|
|
||||||
if attrs["shape"] == prev_shape:
|
|
||||||
score -= 2.0
|
|
||||||
|
|
||||||
# Dramatic shapes: boost for later positions (7-10)
|
|
||||||
if attrs["shape"] in _LATE_SHOW_SHAPES:
|
|
||||||
if position >= 6: # positions 7-10 (0-indexed 6-9)
|
|
||||||
score += 3.0
|
|
||||||
elif position <= 2: # too early
|
|
||||||
score -= 2.0
|
|
||||||
|
|
||||||
if score > best_score:
|
|
||||||
best_score = score
|
|
||||||
best_key = key
|
|
||||||
|
|
||||||
ordered.append(best_key)
|
|
||||||
remaining.remove(best_key)
|
|
||||||
|
|
||||||
session.caller_queue = ordered
|
|
||||||
queue_summary = ", ".join(
|
|
||||||
f"{CALLER_BASES.get(k, {}).get('name', k)}({caller_attrs[k]['energy'][0]}/{caller_attrs[k]['pool'][:4] if caller_attrs[k]['pool'] else '?'}/{caller_attrs[k]['shape'][:4]})"
|
|
||||||
for k in ordered
|
|
||||||
)
|
|
||||||
print(f"[Pacing] Caller queue: {queue_summary}")
|
|
||||||
|
|
||||||
|
|
||||||
def _build_relationship_context():
|
def _build_relationship_context():
|
||||||
"""Find regulars with existing relationships who are both in the current session.
|
"""Find regulars with existing relationships who are both in the current session.
|
||||||
Inject mutual awareness into both callers' prompts."""
|
Inject mutual awareness into both callers' prompts."""
|
||||||
@@ -6059,135 +5761,6 @@ def _build_relationship_context():
|
|||||||
print(f"[Relationships] {regular['name']} knows {other_name} ({rel_type})")
|
print(f"[Relationships] {regular['name']} knows {other_name} ({rel_type})")
|
||||||
|
|
||||||
|
|
||||||
# Style-based TTS speed modifiers — stacks with per-voice and per-utterance adjustments
|
|
||||||
STYLE_SPEED_MODIFIERS = {
|
|
||||||
"quiet_nervous": -0.1,
|
|
||||||
"first_time": -0.08,
|
|
||||||
"emotional": -0.1,
|
|
||||||
"world_weary": -0.15,
|
|
||||||
"philosopher": -0.08,
|
|
||||||
"storyteller": -0.05,
|
|
||||||
"high_energy": +0.1,
|
|
||||||
"confrontational": +0.08,
|
|
||||||
"angry_venting": +0.08,
|
|
||||||
"rambling": +0.05,
|
|
||||||
"comedian": +0.05,
|
|
||||||
}
|
|
||||||
|
|
||||||
# Style-based phone filter quality
|
|
||||||
STYLE_PHONE_QUALITY = {
|
|
||||||
"quiet_nervous": "bad",
|
|
||||||
"mysterious": "bad",
|
|
||||||
"world_weary": "bad",
|
|
||||||
"conspiracy": "bad",
|
|
||||||
"high_energy": "good",
|
|
||||||
"confrontational": "good",
|
|
||||||
"bragger": "good",
|
|
||||||
"comedian": "good",
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
def _normalize_style_key(style: str) -> str:
|
|
||||||
"""Convert a full CALLER_STYLES string to its short key from CALLER_STYLE_KEYS.
|
|
||||||
|
|
||||||
Works by finding the style in the CALLER_STYLES list and returning
|
|
||||||
the corresponding CALLER_STYLE_KEYS entry. Falls back to the input
|
|
||||||
lowered if the style is already a short key or not found."""
|
|
||||||
if not style:
|
|
||||||
return ""
|
|
||||||
style_lower = style.strip().lower()
|
|
||||||
if style_lower in CALLER_STYLE_KEYS:
|
|
||||||
return style_lower
|
|
||||||
for i, full_style in enumerate(CALLER_STYLES):
|
|
||||||
if style == full_style:
|
|
||||||
return CALLER_STYLE_KEYS[i]
|
|
||||||
for i, full_style in enumerate(CALLER_STYLES):
|
|
||||||
if style_lower in full_style.lower():
|
|
||||||
return CALLER_STYLE_KEYS[i]
|
|
||||||
return style_lower
|
|
||||||
|
|
||||||
|
|
||||||
def _match_voices_to_styles():
|
|
||||||
"""Re-assign voices to match caller communication styles after backgrounds are generated."""
|
|
||||||
from .services.tts import VOICE_PROFILES
|
|
||||||
|
|
||||||
for key, base in CALLER_BASES.items():
|
|
||||||
if base.get("returning"):
|
|
||||||
continue
|
|
||||||
|
|
||||||
style_raw = session.caller_styles.get(key, "")
|
|
||||||
if not style_raw:
|
|
||||||
continue
|
|
||||||
|
|
||||||
style_key = _normalize_style_key(style_raw)
|
|
||||||
prefs = STYLE_VOICE_PREFERENCES.get(style_key)
|
|
||||||
if not prefs:
|
|
||||||
continue
|
|
||||||
|
|
||||||
# Override age_feel based on caller's actual age from background
|
|
||||||
bg = session.caller_backgrounds.get(key)
|
|
||||||
if isinstance(bg, CallerBackground) and bg.age:
|
|
||||||
prefs = dict(prefs) # copy before mutating
|
|
||||||
if bg.age >= 50:
|
|
||||||
prefs["age_feel"] = "mature"
|
|
||||||
elif bg.age >= 35:
|
|
||||||
prefs["age_feel"] = "middle"
|
|
||||||
elif bg.age < 25:
|
|
||||||
prefs["age_feel"] = "young"
|
|
||||||
|
|
||||||
gender = base["gender"]
|
|
||||||
pool = INWORLD_MALE_VOICES if gender == "male" else INWORLD_FEMALE_VOICES
|
|
||||||
voice_pool = [v for v in pool if v not in BLACKLISTED_VOICES]
|
|
||||||
|
|
||||||
scored = []
|
|
||||||
for voice_name in voice_pool:
|
|
||||||
profile = VOICE_PROFILES.get(voice_name)
|
|
||||||
if not profile:
|
|
||||||
scored.append((voice_name, 0))
|
|
||||||
continue
|
|
||||||
score = 0
|
|
||||||
for dim in ["weight", "energy", "warmth", "age_feel"]:
|
|
||||||
pref_val = prefs.get(dim)
|
|
||||||
if pref_val and profile.get(dim) == pref_val:
|
|
||||||
score += 1
|
|
||||||
scored.append((voice_name, score))
|
|
||||||
|
|
||||||
if scored:
|
|
||||||
names = [s[0] for s in scored]
|
|
||||||
weights = [max(1, s[1] * 3) for s in scored]
|
|
||||||
chosen = random.choices(names, weights=weights, k=1)[0]
|
|
||||||
|
|
||||||
used_voices = {CALLER_BASES[k]["voice"] for k in CALLER_BASES if k != key and "voice" in CALLER_BASES[k]}
|
|
||||||
if chosen in used_voices:
|
|
||||||
alternatives = [(n, w) for n, w in zip(names, weights) if n not in used_voices]
|
|
||||||
if alternatives:
|
|
||||||
alt_names, alt_weights = zip(*alternatives)
|
|
||||||
chosen = random.choices(alt_names, weights=alt_weights, k=1)[0]
|
|
||||||
|
|
||||||
old_voice = base.get("voice", "")
|
|
||||||
base["voice"] = chosen
|
|
||||||
if old_voice != chosen:
|
|
||||||
print(f"[VoiceMatch] {base.get('name', key)}: {old_voice} → {chosen} (style: {style_key})")
|
|
||||||
|
|
||||||
|
|
||||||
def get_style_speed_modifier(caller_key: str) -> float:
|
|
||||||
"""Get the TTS speed modifier for a caller based on their communication style."""
|
|
||||||
style_raw = session.caller_styles.get(caller_key, "")
|
|
||||||
if not style_raw:
|
|
||||||
return 0.0
|
|
||||||
style_key = _normalize_style_key(style_raw)
|
|
||||||
return STYLE_SPEED_MODIFIERS.get(style_key, 0.0)
|
|
||||||
|
|
||||||
|
|
||||||
def get_style_phone_quality(caller_key: str) -> str | None:
|
|
||||||
"""Get the phone filter quality override for a caller based on their style."""
|
|
||||||
style_raw = session.caller_styles.get(caller_key, "")
|
|
||||||
if not style_raw:
|
|
||||||
return None
|
|
||||||
style_key = _normalize_style_key(style_raw)
|
|
||||||
return STYLE_PHONE_QUALITY.get(style_key)
|
|
||||||
|
|
||||||
|
|
||||||
# Known topics for smarter search queries — maps keywords in backgrounds to search terms
|
# Known topics for smarter search queries — maps keywords in backgrounds to search terms
|
||||||
_TOPIC_SEARCH_MAP = [
|
_TOPIC_SEARCH_MAP = [
|
||||||
# TV shows
|
# TV shows
|
||||||
@@ -6608,9 +6181,6 @@ def _deserialize_call_record(data: dict) -> CallRecord:
|
|||||||
def _assess_call_quality(
|
def _assess_call_quality(
|
||||||
conversation: list[dict],
|
conversation: list[dict],
|
||||||
caller_hangup: bool = False,
|
caller_hangup: bool = False,
|
||||||
shape: str = "",
|
|
||||||
style: str = "",
|
|
||||||
pool_name: str = "",
|
|
||||||
) -> dict:
|
) -> dict:
|
||||||
"""Compute heuristic quality signals for a completed call. No LLM needed.
|
"""Compute heuristic quality signals for a completed call. No LLM needed.
|
||||||
Returns a plain dict for storage in CallRecord.quality_signals and session.call_quality_signals."""
|
Returns a plain dict for storage in CallRecord.quality_signals and session.call_quality_signals."""
|
||||||
@@ -6639,9 +6209,6 @@ def _assess_call_quality(
|
|||||||
"host_engagement": host_engagement,
|
"host_engagement": host_engagement,
|
||||||
"caller_depth": caller_depth,
|
"caller_depth": caller_depth,
|
||||||
"natural_ending": natural_ending,
|
"natural_ending": natural_ending,
|
||||||
"shape": shape,
|
|
||||||
"style": style,
|
|
||||||
"pool_name": pool_name,
|
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
||||||
@@ -6661,8 +6228,6 @@ class Session:
|
|||||||
self._research_task: asyncio.Task | None = None
|
self._research_task: asyncio.Task | None = None
|
||||||
self.used_reasons: set[str] = set() # Track used caller reasons to prevent repeats
|
self.used_reasons: set[str] = set() # Track used caller reasons to prevent repeats
|
||||||
self.pool_weights: dict[str, float] = _generate_pool_weights()
|
self.pool_weights: dict[str, float] = _generate_pool_weights()
|
||||||
self.caller_styles: dict[str, str] = {}
|
|
||||||
self.caller_shapes: dict[str, str] = {}
|
|
||||||
self.tone_streak: list[str] = [] # Track tone per call for variety balancing
|
self.tone_streak: list[str] = [] # Track tone per call for variety balancing
|
||||||
self.call_quality_signals: list[dict] = [] # Per-call quality heuristics for tuning
|
self.call_quality_signals: list[dict] = [] # Per-call quality heuristics for tuning
|
||||||
self._caller_hangup: bool = False # Set when [HANGUP] sentinel detected in current call
|
self._caller_hangup: bool = False # Set when [HANGUP] sentinel detected in current call
|
||||||
@@ -6672,50 +6237,6 @@ class Session:
|
|||||||
self.relationship_context: dict[str, str] = {} # caller_key → relationship prompt injection
|
self.relationship_context: dict[str, str] = {} # caller_key → relationship prompt injection
|
||||||
self.intern_monitoring: bool = True # Devon monitors conversations by default
|
self.intern_monitoring: bool = True # Devon monitors conversations by default
|
||||||
self.show_theme: str = "" # Current show theme (e.g. "St. Patrick's Day")
|
self.show_theme: str = "" # Current show theme (e.g. "St. Patrick's Day")
|
||||||
# Caller model routing
|
|
||||||
self.caller_model_strategy: str = "style_matched" # "single" | "cycle" | "style_matched"
|
|
||||||
self.caller_model_pool: list[str] = [
|
|
||||||
"x-ai/grok-4.1-fast", # edgy, casual ($0.20/$0.50)
|
|
||||||
"x-ai/grok-4", # deep edgy reasoning ($3/$15)
|
|
||||||
"anthropic/claude-sonnet-4.6", # empathetic, nuanced ($3/$15)
|
|
||||||
"moonshotai/kimi-k2", # creative, warm, expressive ($0.60/$2)
|
|
||||||
"mistralai/mistral-large-2512", # dry wit, precise ($0.50/$1.50)
|
|
||||||
"deepseek/deepseek-chat-v3-0324", # direct, unfiltered ($0.27/$1.10)
|
|
||||||
"qwen/qwen3-235b-a22b", # meandering storyteller ($0.20/$0.60)
|
|
||||||
"google/gemini-2.5-pro", # articulate, analytical ($1.25/$10)
|
|
||||||
"meta-llama/llama-3.3-70b-instruct", # casual, natural hesitation ($0.10/$0.32)
|
|
||||||
]
|
|
||||||
self.caller_model_map: dict[str, str] = {
|
|
||||||
# Grok 4.1 Fast — high-energy swagger, edgy humor, fast
|
|
||||||
"high_energy": "x-ai/grok-4.1-fast",
|
|
||||||
"bragger": "x-ai/grok-4.1-fast",
|
|
||||||
"comedian": "x-ai/grok-4.1-fast",
|
|
||||||
# Grok 4 Full — deep reasoning for confrontation and arguments
|
|
||||||
"confrontational": "x-ai/grok-4",
|
|
||||||
# DeepSeek Chat — raw, direct, no filter
|
|
||||||
"angry_venting": "deepseek/deepseek-chat-v3-0324",
|
|
||||||
# Claude Sonnet 4.6 — genuine vulnerability, emotional depth, nuance
|
|
||||||
"quiet_nervous": "anthropic/claude-sonnet-4.6",
|
|
||||||
"emotional": "anthropic/claude-sonnet-4.6",
|
|
||||||
"sweet_earnest": "moonshotai/kimi-k2",
|
|
||||||
"first_time": "moonshotai/kimi-k2",
|
|
||||||
"world_weary": "anthropic/claude-sonnet-4.6",
|
|
||||||
"philosopher": "anthropic/claude-sonnet-4.6",
|
|
||||||
# Mistral Large — dry, precise, strategic omission
|
|
||||||
"deadpan": "mistralai/mistral-large-2512",
|
|
||||||
"mysterious": "mistralai/mistral-large-2512",
|
|
||||||
# Qwen — loves tangents, detail-rich, born rambler
|
|
||||||
"storyteller": "qwen/qwen3-235b-a22b",
|
|
||||||
"rambling": "qwen/qwen3-235b-a22b",
|
|
||||||
"conspiracy": "qwen/qwen3-235b-a22b",
|
|
||||||
# Grok 4.1 Fast — oversharing energy, can't stop talking
|
|
||||||
"oversharer": "x-ai/grok-4.1-fast",
|
|
||||||
# Mistral Large — pedantic, articulate
|
|
||||||
"know_it_all": "mistralai/mistral-large-2512",
|
|
||||||
}
|
|
||||||
self.caller_model_fallback: str = "anthropic/claude-sonnet-4.6"
|
|
||||||
self.caller_models: dict[str, str] = {} # caller_key → assigned model
|
|
||||||
self._caller_model_cycle_idx: int = 0
|
|
||||||
|
|
||||||
def start_call(self, caller_key: str):
|
def start_call(self, caller_key: str):
|
||||||
self.current_caller_key = caller_key
|
self.current_caller_key = caller_key
|
||||||
@@ -6925,8 +6446,6 @@ class Session:
|
|||||||
"name": base["name"],
|
"name": base["name"],
|
||||||
"voice": base["voice"],
|
"voice": base["voice"],
|
||||||
"vibe": self.get_caller_background(self.current_caller_key),
|
"vibe": self.get_caller_background(self.current_caller_key),
|
||||||
"style": self.caller_styles.get(self.current_caller_key, ""),
|
|
||||||
"shape": self.caller_shapes.get(self.current_caller_key, "standard"),
|
|
||||||
"tts_provider": base.get("tts_provider"),
|
"tts_provider": base.get("tts_provider"),
|
||||||
"emotional_state": emotional_state,
|
"emotional_state": emotional_state,
|
||||||
"energy_level": energy_level,
|
"energy_level": energy_level,
|
||||||
@@ -7003,8 +6522,6 @@ class Session:
|
|||||||
self._research_task.cancel()
|
self._research_task.cancel()
|
||||||
self._research_task = None
|
self._research_task = None
|
||||||
self.pool_weights = _generate_pool_weights()
|
self.pool_weights = _generate_pool_weights()
|
||||||
self.caller_styles = {}
|
|
||||||
self.caller_shapes = {}
|
|
||||||
self.tone_streak = []
|
self.tone_streak = []
|
||||||
self.call_quality_signals = []
|
self.call_quality_signals = []
|
||||||
self._wrapping_up = False
|
self._wrapping_up = False
|
||||||
@@ -7153,19 +6670,11 @@ def _save_checkpoint():
|
|||||||
"research_notes": session.research_notes,
|
"research_notes": session.research_notes,
|
||||||
"caller_bases": caller_bases_snapshot,
|
"caller_bases": caller_bases_snapshot,
|
||||||
"pool_weights": session.pool_weights,
|
"pool_weights": session.pool_weights,
|
||||||
"caller_styles": session.caller_styles,
|
|
||||||
"caller_shapes": session.caller_shapes,
|
|
||||||
"tone_streak": session.tone_streak,
|
"tone_streak": session.tone_streak,
|
||||||
"call_quality_signals": session.call_quality_signals,
|
"call_quality_signals": session.call_quality_signals,
|
||||||
"caller_queue": session.caller_queue,
|
"caller_queue": session.caller_queue,
|
||||||
"relationship_context": session.relationship_context,
|
"relationship_context": session.relationship_context,
|
||||||
"intern_monitoring": session.intern_monitoring,
|
"intern_monitoring": session.intern_monitoring,
|
||||||
"caller_model_strategy": session.caller_model_strategy,
|
|
||||||
"caller_model_pool": session.caller_model_pool,
|
|
||||||
"caller_model_map": session.caller_model_map,
|
|
||||||
"caller_model_fallback": session.caller_model_fallback,
|
|
||||||
"caller_models": session.caller_models,
|
|
||||||
"caller_model_cycle_idx": session._caller_model_cycle_idx,
|
|
||||||
"costs": cost_tracker.get_live_summary(),
|
"costs": cost_tracker.get_live_summary(),
|
||||||
"cost_records": {
|
"cost_records": {
|
||||||
"llm": [asdict(r) for r in cost_tracker.llm_records],
|
"llm": [asdict(r) for r in cost_tracker.llm_records],
|
||||||
@@ -7205,29 +6714,11 @@ def _load_checkpoint() -> bool:
|
|||||||
session.news_headlines = data.get("news_headlines", [])
|
session.news_headlines = data.get("news_headlines", [])
|
||||||
session.research_notes = data.get("research_notes", {})
|
session.research_notes = data.get("research_notes", {})
|
||||||
session.pool_weights = data.get("pool_weights", _generate_pool_weights())
|
session.pool_weights = data.get("pool_weights", _generate_pool_weights())
|
||||||
session.caller_styles = data.get("caller_styles", {})
|
|
||||||
session.caller_shapes = data.get("caller_shapes", {})
|
|
||||||
session.tone_streak = data.get("tone_streak", [])
|
session.tone_streak = data.get("tone_streak", [])
|
||||||
session.call_quality_signals = data.get("call_quality_signals", [])
|
session.call_quality_signals = data.get("call_quality_signals", [])
|
||||||
session.caller_queue = data.get("caller_queue", [])
|
session.caller_queue = data.get("caller_queue", [])
|
||||||
session.relationship_context = data.get("relationship_context", {})
|
session.relationship_context = data.get("relationship_context", {})
|
||||||
session.intern_monitoring = data.get("intern_monitoring", True)
|
session.intern_monitoring = data.get("intern_monitoring", True)
|
||||||
session.caller_model_strategy = data.get("caller_model_strategy", "style_matched")
|
|
||||||
# Use fresh defaults if checkpoint has stale/empty model config
|
|
||||||
fresh = Session()
|
|
||||||
saved_pool = data.get("caller_model_pool", [])
|
|
||||||
saved_map = data.get("caller_model_map", {})
|
|
||||||
# Detect stale config: check if saved models are in the current OPENROUTER_MODELS list
|
|
||||||
from backend.services.llm import OPENROUTER_MODELS
|
|
||||||
valid_models = set(OPENROUTER_MODELS)
|
|
||||||
saved_map_models = set(saved_map.values()) if saved_map else set()
|
|
||||||
pool_has_invalid = saved_pool and not all(m in valid_models for m in saved_pool)
|
|
||||||
map_has_invalid = saved_map_models and not all(m in valid_models for m in saved_map_models)
|
|
||||||
session.caller_model_pool = saved_pool if saved_pool and not pool_has_invalid else fresh.caller_model_pool
|
|
||||||
session.caller_model_map = saved_map if saved_map and not map_has_invalid else fresh.caller_model_map
|
|
||||||
session.caller_model_fallback = fresh.caller_model_fallback if data.get("caller_model_fallback", "") not in valid_models else data["caller_model_fallback"]
|
|
||||||
session.caller_models = data.get("caller_models", {})
|
|
||||||
session._caller_model_cycle_idx = data.get("caller_model_cycle_idx", 0)
|
|
||||||
for key, snapshot in data.get("caller_bases", {}).items():
|
for key, snapshot in data.get("caller_bases", {}).items():
|
||||||
if key in CALLER_BASES:
|
if key in CALLER_BASES:
|
||||||
CALLER_BASES[key]["name"] = snapshot["name"]
|
CALLER_BASES[key]["name"] = snapshot["name"]
|
||||||
@@ -8589,9 +8080,6 @@ async def _summarize_ai_call(caller_key: str, caller_name: str, conversation: li
|
|||||||
quality_signals = _assess_call_quality(
|
quality_signals = _assess_call_quality(
|
||||||
conversation,
|
conversation,
|
||||||
caller_hangup=caller_hangup,
|
caller_hangup=caller_hangup,
|
||||||
shape="",
|
|
||||||
style=comm_style,
|
|
||||||
pool_name="",
|
|
||||||
)
|
)
|
||||||
session.call_quality_signals.append(quality_signals)
|
session.call_quality_signals.append(quality_signals)
|
||||||
|
|
||||||
@@ -8611,7 +8099,7 @@ async def _summarize_ai_call(caller_key: str, caller_name: str, conversation: li
|
|||||||
key_details=key_dets,
|
key_details=key_dets,
|
||||||
))
|
))
|
||||||
print(f"[AI Summary] {caller_name} call summarized: {summary[:80]}...")
|
print(f"[AI Summary] {caller_name} call summarized: {summary[:80]}...")
|
||||||
print(f"[Quality] {caller_name}: exchanges={quality_signals['exchange_count']} avg_len={quality_signals['avg_response_length']:.0f}c host_engagement={quality_signals['host_engagement']} caller_depth={quality_signals['caller_depth']} natural_end={quality_signals['natural_ending']} shape={quality_signals['shape']} style={quality_signals['style']} pool={quality_signals['pool_name']}")
|
print(f"[Quality] {caller_name}: exchanges={quality_signals['exchange_count']} avg_len={quality_signals['avg_response_length']:.0f}c host_engagement={quality_signals['host_engagement']} caller_depth={quality_signals['caller_depth']} natural_end={quality_signals['natural_ending']}")
|
||||||
|
|
||||||
# Returning caller promotion/update logic
|
# Returning caller promotion/update logic
|
||||||
try:
|
try:
|
||||||
@@ -8623,7 +8111,6 @@ async def _summarize_ai_call(caller_key: str, caller_name: str, conversation: li
|
|||||||
elif len(conversation) >= 8 and random.random() < 0.05:
|
elif len(conversation) >= 8 and random.random() < 0.05:
|
||||||
# 5% chance to promote first-timer with 8+ messages
|
# 5% chance to promote first-timer with 8+ messages
|
||||||
bg = session.caller_backgrounds.get(caller_key, "")
|
bg = session.caller_backgrounds.get(caller_key, "")
|
||||||
caller_style = session.caller_styles.get(caller_key, "")
|
|
||||||
|
|
||||||
if isinstance(bg, CallerBackground):
|
if isinstance(bg, CallerBackground):
|
||||||
# Clean extraction from structured data
|
# Clean extraction from structured data
|
||||||
@@ -8660,7 +8147,7 @@ async def _summarize_ai_call(caller_key: str, caller_name: str, conversation: li
|
|||||||
personality_traits=traits[:4],
|
personality_traits=traits[:4],
|
||||||
first_call_summary=summary,
|
first_call_summary=summary,
|
||||||
voice=base.get("voice"),
|
voice=base.get("voice"),
|
||||||
stable_seeds={"style": caller_style},
|
stable_seeds={},
|
||||||
structured_background=structured_bg,
|
structured_background=structured_bg,
|
||||||
avatar=avatar_path.name if avatar_path else None,
|
avatar=avatar_path.name if avatar_path else None,
|
||||||
)
|
)
|
||||||
@@ -8721,29 +8208,16 @@ def _detect_caller_relationships(caller_key: str, caller_name: str,
|
|||||||
import re
|
import re
|
||||||
|
|
||||||
|
|
||||||
def _pick_response_budget(shape: str = "standard", wrapping_up: bool = False) -> tuple[int, int]:
|
def _pick_response_budget(wrapping_up: bool = False) -> tuple[int, int]:
|
||||||
"""Pick a random max_tokens and sentence cap for response variety.
|
"""Pick a random max_tokens and sentence cap for response variety.
|
||||||
Returns (max_tokens, max_sentences).
|
Returns (max_tokens, max_sentences).
|
||||||
Keeps responses conversational but gives room for real answers.
|
Keeps responses conversational but gives room for real answers.
|
||||||
Token budget is intentionally generous to avoid mid-sentence cutoffs —
|
Token budget is intentionally generous to avoid mid-sentence cutoffs —
|
||||||
the sentence cap controls actual length.
|
the sentence cap controls actual length."""
|
||||||
Shape overrides the default distribution for certain call types."""
|
|
||||||
|
|
||||||
if wrapping_up:
|
if wrapping_up:
|
||||||
return 200, 2
|
return 200, 2
|
||||||
|
|
||||||
# Shape-specific overrides
|
|
||||||
if shape == "quick_hit":
|
|
||||||
return random.choice([(450, 4), (500, 5)])
|
|
||||||
elif shape == "escalating_reveal":
|
|
||||||
roll = random.random()
|
|
||||||
if roll < 0.4:
|
|
||||||
return 600, 6 # 40% — tight, forces restraint
|
|
||||||
else:
|
|
||||||
return 800, 8 # 60% — room to build but capped
|
|
||||||
elif shape == "confrontation":
|
|
||||||
return random.choice([(700, 6), (800, 8)])
|
|
||||||
|
|
||||||
# Default distribution — give callers room to tell their story
|
# Default distribution — give callers room to tell their story
|
||||||
roll = random.random()
|
roll = random.random()
|
||||||
if roll < 0.10:
|
if roll < 0.10:
|
||||||
@@ -9171,16 +8645,6 @@ def _apply_pronunciation_fixes(text: str) -> str:
|
|||||||
return text
|
return text
|
||||||
|
|
||||||
|
|
||||||
_INFORMAL_STYLES = {"nervous", "scattered", "rambling", "high-energy", "comedian",
|
|
||||||
"angry", "venting", "confrontational"}
|
|
||||||
|
|
||||||
|
|
||||||
def _is_informal_style(style: str) -> bool:
|
|
||||||
"""Check if a caller style should keep colloquialisms."""
|
|
||||||
style_lower = style.lower()
|
|
||||||
return any(s in style_lower for s in _INFORMAL_STYLES)
|
|
||||||
|
|
||||||
|
|
||||||
def clean_for_tts(text: str, formal: bool = True) -> str:
|
def clean_for_tts(text: str, formal: bool = True) -> str:
|
||||||
"""Strip out non-speakable content and fix phonetic spellings for TTS.
|
"""Strip out non-speakable content and fix phonetic spellings for TTS.
|
||||||
When formal=False, keeps colloquialisms (gonna, kinda, etc.) for natural-sounding callers."""
|
When formal=False, keeps colloquialisms (gonna, kinda, etc.) for natural-sounding callers."""
|
||||||
@@ -9441,8 +8905,7 @@ async def chat(request: ChatRequest):
|
|||||||
slim_caller = session.caller_backgrounds.get(session.current_caller_key, {})
|
slim_caller = session.caller_backgrounds.get(session.current_caller_key, {})
|
||||||
system_prompt = get_caller_prompt(slim_caller)
|
system_prompt = get_caller_prompt(slim_caller)
|
||||||
|
|
||||||
call_shape = session.caller.get("shape", "standard") if session.caller else "standard"
|
max_tokens, max_sentences = _pick_response_budget(wrapping_up=is_wrapping)
|
||||||
max_tokens, max_sentences = _pick_response_budget(call_shape, wrapping_up=is_wrapping)
|
|
||||||
messages = _normalize_messages_for_llm(session.conversation[-_dynamic_context_window():])
|
messages = _normalize_messages_for_llm(session.conversation[-_dynamic_context_window():])
|
||||||
_caller_name = session.caller.get("name", "") if session.caller else ""
|
_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 = session.get_caller_model(session.current_caller_key) if session.current_caller_key else None
|
||||||
@@ -9476,11 +8939,10 @@ async def chat(request: ChatRequest):
|
|||||||
print(f"[Chat] Discarding stale response (epoch {epoch} → {_session_epoch})")
|
print(f"[Chat] Discarding stale response (epoch {epoch} → {_session_epoch})")
|
||||||
raise HTTPException(409, "Call changed during response")
|
raise HTTPException(409, "Call changed during response")
|
||||||
|
|
||||||
print(f"[Chat] Raw LLM ({max_tokens}tok/{max_sentences}s, shape={call_shape}): {response[:100] if response else '(empty)'}...")
|
print(f"[Chat] Raw LLM ({max_tokens}tok/{max_sentences}s): {response[:100] if response else '(empty)'}...")
|
||||||
|
|
||||||
# Clean response for TTS (remove parenthetical actions, asterisks, etc.)
|
# Clean response for TTS (remove parenthetical actions, asterisks, etc.)
|
||||||
caller_style = session.caller.get("style", "") if session.caller else ""
|
response = clean_for_tts(response, formal=False)
|
||||||
response = clean_for_tts(response, formal=not _is_informal_style(caller_style))
|
|
||||||
response = _trim_to_sentences(response, max_sentences)
|
response = _trim_to_sentences(response, max_sentences)
|
||||||
response = ensure_complete_thought(response)
|
response = ensure_complete_thought(response)
|
||||||
|
|
||||||
@@ -9489,7 +8951,7 @@ async def chat(request: ChatRequest):
|
|||||||
if caller_hangup:
|
if caller_hangup:
|
||||||
response = response.replace("[HANGUP]", "").strip()
|
response = response.replace("[HANGUP]", "").strip()
|
||||||
session._caller_hangup = True
|
session._caller_hangup = True
|
||||||
print(f"[Chat] Caller hangup detected (shape={call_shape})")
|
print(f"[Chat] Caller hangup detected")
|
||||||
|
|
||||||
print(f"[Chat] Cleaned: {response[:100] if response else '(empty)'}...")
|
print(f"[Chat] Cleaned: {response[:100] if response else '(empty)'}...")
|
||||||
|
|
||||||
@@ -10404,8 +9866,7 @@ async def _trigger_ai_auto_respond(accumulated_text: str):
|
|||||||
slim_caller = session.caller_backgrounds.get(session.current_caller_key, {})
|
slim_caller = session.caller_backgrounds.get(session.current_caller_key, {})
|
||||||
system_prompt = get_caller_prompt(slim_caller)
|
system_prompt = get_caller_prompt(slim_caller)
|
||||||
|
|
||||||
call_shape = session.caller.get("shape", "standard") if session.caller else "standard"
|
max_tokens, max_sentences = _pick_response_budget(wrapping_up=is_wrapping)
|
||||||
max_tokens, max_sentences = _pick_response_budget(call_shape, wrapping_up=is_wrapping)
|
|
||||||
messages = _normalize_messages_for_llm(session.conversation[-_dynamic_context_window():])
|
messages = _normalize_messages_for_llm(session.conversation[-_dynamic_context_window():])
|
||||||
_caller_name = session.caller.get("name", "") if session.caller else ""
|
_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 = session.get_caller_model(session.current_caller_key) if session.current_caller_key else None
|
||||||
@@ -10440,8 +9901,7 @@ async def _trigger_ai_auto_respond(accumulated_text: str):
|
|||||||
broadcast_event("ai_done")
|
broadcast_event("ai_done")
|
||||||
return
|
return
|
||||||
|
|
||||||
auto_style = session.caller.get("style", "") if session.caller else ""
|
response = clean_for_tts(response, formal=False)
|
||||||
response = clean_for_tts(response, formal=not _is_informal_style(auto_style))
|
|
||||||
response = _trim_to_sentences(response, max_sentences)
|
response = _trim_to_sentences(response, max_sentences)
|
||||||
response = ensure_complete_thought(response)
|
response = ensure_complete_thought(response)
|
||||||
|
|
||||||
@@ -10526,8 +9986,7 @@ async def ai_respond():
|
|||||||
slim_caller = session.caller_backgrounds.get(session.current_caller_key, {})
|
slim_caller = session.caller_backgrounds.get(session.current_caller_key, {})
|
||||||
system_prompt = get_caller_prompt(slim_caller)
|
system_prompt = get_caller_prompt(slim_caller)
|
||||||
|
|
||||||
call_shape = session.caller.get("shape", "standard") if session.caller else "standard"
|
max_tokens, max_sentences = _pick_response_budget(wrapping_up=is_wrapping)
|
||||||
max_tokens, max_sentences = _pick_response_budget(call_shape, wrapping_up=is_wrapping)
|
|
||||||
messages = _normalize_messages_for_llm(session.conversation[-_dynamic_context_window():])
|
messages = _normalize_messages_for_llm(session.conversation[-_dynamic_context_window():])
|
||||||
_caller_name = session.caller.get("name", "") if session.caller else ""
|
_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 = session.get_caller_model(session.current_caller_key) if session.current_caller_key else None
|
||||||
@@ -10559,8 +10018,7 @@ async def ai_respond():
|
|||||||
if _session_epoch != epoch:
|
if _session_epoch != epoch:
|
||||||
raise HTTPException(409, "Call changed during response")
|
raise HTTPException(409, "Call changed during response")
|
||||||
|
|
||||||
ai_style = session.caller.get("style", "") if session.caller else ""
|
response = clean_for_tts(response, formal=False)
|
||||||
response = clean_for_tts(response, formal=not _is_informal_style(ai_style))
|
|
||||||
response = _trim_to_sentences(response, max_sentences)
|
response = _trim_to_sentences(response, max_sentences)
|
||||||
response = ensure_complete_thought(response)
|
response = ensure_complete_thought(response)
|
||||||
|
|
||||||
|
|||||||
Reference in New Issue
Block a user