Skip voice altogether and use the same code for text-only integrations
+Agent that ignores backchannel words ("yeah", "ok") while speaking but responds to commands ("stop", "wait")
Skip voice altogether and use the same code for text-only integrations
++Code +
+Produce transcriptions from all users in the room
diff --git a/examples/voice_agents/intelligent_interruption_demo.py b/examples/voice_agents/intelligent_interruption_demo.py new file mode 100644 index 0000000000..44e3b50c34 --- /dev/null +++ b/examples/voice_agents/intelligent_interruption_demo.py @@ -0,0 +1,111 @@ +""" +Intelligent Interruption Handling Demo +====================================== + +This example demonstrates the intelligent interruption handling feature that +distinguishes between passive acknowledgements ("yeah", "ok", "hmm") and +intentional interruptions ("stop", "wait", "no"). + +Key behaviors: +1. When the agent is speaking and the user says "yeah/ok/hmm" -> Agent continues uninterrupted +2. When the agent is speaking and the user says "stop/wait/no" -> Agent stops immediately +3. When the agent is silent and the user says "yeah" -> Agent responds normally + +Features: +- Configurable fuzzy matching threshold (default 80%, range 0-100) +- Handles typos and misspellings ("yeahh", "okayy", "yea") +- STT transcription variations ("yah" vs "yeah") +- Common phonetic variations +- Robust error handling for fuzzy matching failures +- Case-insensitive matching +- Punctuation handling + +Configuration: + # Default threshold (80%) + session = AgentSession(...) + + # Stricter matching (requires closer matches) + session = AgentSession(fuzzy_match_threshold=90, ...) + + # More lenient matching (allows more variations) + session = AgentSession(fuzzy_match_threshold=70, ...) + +Usage: + uv run examples/voice_agents/intelligent_interruption_demo.py console + + # Text mode (no microphone required) + uv run examples/voice_agents/intelligent_interruption_demo.py console --text + +Environment variables: + LIVEKIT_URL - Your LiveKit server URL + LIVEKIT_API_KEY - Your LiveKit API key + LIVEKIT_API_SECRET - Your LiveKit API secret + OPENAI_API_KEY - Your OpenAI API key (for LLM and TTS) + + # Optional: Customize ignored words (comma-separated) + LIVEKIT_AGENT_IGNORED_WORDS - e.g., "yeah,ok,hmm,right,uh-huh" +""" + +import os +from pathlib import Path +from dotenv import load_dotenv + +# Load .env from examples directory +env_path = Path(__file__).parent.parent / ".env" +load_dotenv(dotenv_path=env_path) + +from livekit.agents import Agent, AgentSession, JobContext, WorkerOptions, cli +from livekit.plugins import deepgram, openai, silero + + +async def entrypoint(ctx: JobContext): + await ctx.connect() + + # Create an agent with a long explanation prompt to test interruption handling + agent = Agent( + instructions="""You are a helpful voice assistant demonstrating intelligent interruption handling. + +When asked to explain something, give a LONG, detailed explanation (at least 30 seconds of speech). +This helps demonstrate that you can continue speaking even when the user says "yeah", "ok", or "hmm" +to acknowledge they're listening. + +Example topics you can explain in detail: +- The history of the internet +- How airplanes fly +- The water cycle +- Photosynthesis +- How computers work + +When the user says things like "stop", "wait", "hold on", or "no", you should stop immediately +and listen to what they have to say. + +Start by greeting the user and offering to explain a topic in detail.""", + ) + + # Create the agent session with intelligent interruption handling + # The ignored_words list can be customized here or via environment variable + # The fuzzy_match_threshold can be adjusted (default 80, range 0-100) + session = AgentSession( + vad=silero.VAD.load(), + stt=deepgram.STT(), + llm=openai.LLM(), + tts=openai.TTS(), + # Customize the ignored words list if needed (uses defaults if not specified) + # ignored_words=["yeah", "ok", "hmm", "right", "uh-huh", "mhm", "sure"], + # Customize fuzzy matching threshold (default 80) + # fuzzy_match_threshold=90, # Stricter: requires closer matches + # fuzzy_match_threshold=70, # More lenient: allows more variations + ) + + # Log the current configuration + print(f"\n🎤 Ignored words (backchannel): {list(session.options.ignored_words)}") + print(f"📊 Fuzzy match threshold: {session.options.fuzzy_match_threshold}%") + print(" These words will NOT interrupt the agent while it's speaking.") + print("\n💡 Try saying 'yeah' or 'ok' while the agent is talking - it will continue!") + print(" But saying 'stop' or 'wait' will interrupt immediately.\n") + + await session.start(agent=agent, room=ctx.room) + + +if __name__ == "__main__": + cli.run_app(WorkerOptions(entrypoint_fnc=entrypoint)) diff --git a/livekit-agents/livekit/agents/voice/agent_activity.py b/livekit-agents/livekit/agents/voice/agent_activity.py index 0c3f7c743d..9dd664fed0 100644 --- a/livekit-agents/livekit/agents/voice/agent_activity.py +++ b/livekit-agents/livekit/agents/voice/agent_activity.py @@ -9,6 +9,9 @@ from dataclasses import dataclass from typing import TYPE_CHECKING, Any, Optional, Union, cast +import re +from rapidfuzz import fuzz, process + from opentelemetry import context as otel_context, trace from livekit import rtc @@ -236,6 +239,98 @@ def _validate_turn_detection( return mode + def _is_soft_input(self, text: str) -> bool: + """ + Check if the given text consists only of ignored/backchannel words. + + Uses fuzzy matching with a similarity threshold of 80% to handle: + - Typos and misspellings ("yeahh", "okayy", "yea") + - STT transcription variations ("yah" vs "yeah") + - Common phonetic variations + + Returns True if all words in the text match ignored_words (exactly or fuzzily), + meaning this is likely a passive acknowledgement rather than an + intentional interruption. + """ + if not text: + return True # Empty text is considered soft + + ignored_words = set(w.lower() for w in self._session.options.ignored_words) + if not ignored_words: + return False # No ignored words configured, nothing is soft + + # Normalize and extract words from the transcript + normalized = text.lower().strip() + # Split on whitespace and punctuation, keep only alphanumeric words + words = re.findall(r"[a-z0-9]+(?:[-'][a-z0-9]+)?", normalized) + + if not words: + return True # No actual words found + + # Use configurable fuzzy matching threshold from session options + SIMILARITY_THRESHOLD = self._session.options.fuzzy_match_threshold + + # Check if ALL words are in the ignored list (exact or fuzzy match) + for word in words: + # Try exact match first (faster) + if word in ignored_words: + continue + + # Try fuzzy match with threshold using extractOne for efficiency + try: + best_match = process.extractOne( + word, + ignored_words, + scorer=fuzz.ratio, + score_cutoff=SIMILARITY_THRESHOLD + ) + + if not best_match: + return False # Found a word that doesn't match any ignored word + except Exception as e: + # If fuzzy matching fails, log error and fall back to allowing interruption + logger.error( + "fuzzy matching failed, allowing interruption", + exc_info=e, + extra={"word": word, "transcript": text} + ) + return False # Safely allow interruption on error + + logger.debug( + "soft input detected, ignoring interruption", + extra={"transcript": text, "words": words} + ) + return True # All words matched (exactly or fuzzily) + + def _should_ignore_interruption(self, transcript: str | None = None) -> bool: + """ + Determine if an interruption should be ignored based on agent state and transcript. + + Returns True if: + - Agent is currently speaking (has active, uninterrupted speech) + - The transcript (if available) consists only of backchannel words + """ + # Agent must be speaking and have active, non-interrupted speech + if ( + self._current_speech is None + or self._current_speech.interrupted + or not self._current_speech.allow_interruptions + ): + return False # Agent is not speaking or already interrupted + + # If we have a transcript, check if it's soft input + if transcript is not None: + return self._is_soft_input(transcript) + + # If STT is available, check the current transcript from audio recognition + if self._audio_recognition is not None: + current = self._audio_recognition.current_transcript + if current: + return self._is_soft_input(current) + + # No transcript available - cannot determine, allow interruption + return False + @property def scheduling_paused(self) -> bool: return self._scheduling_paused @@ -1166,7 +1261,7 @@ def _on_generation_created(self, ev: llm.GenerationCreatedEvent) -> None: ) self._schedule_speech(handle, SpeechHandle.SPEECH_PRIORITY_NORMAL) - def _interrupt_by_audio_activity(self) -> None: + def _interrupt_by_audio_activity(self, *, transcript: str | None = None) -> None: opt = self._session.options use_pause = opt.resume_false_interruption and opt.false_interruption_timeout is not None @@ -1174,6 +1269,15 @@ def _interrupt_by_audio_activity(self) -> None: # ignore if realtime model has turn detection enabled return + # Check for soft/backchannel input - if the agent is speaking and the + # user only said ignored words, skip the interruption entirely + if self._should_ignore_interruption(transcript): + logger.debug( + "ignoring soft input while agent is speaking", + extra={"transcript": transcript or self._audio_recognition.current_transcript if self._audio_recognition else ""}, + ) + return + if ( self.stt is not None and opt.min_interruption_words > 0 @@ -1248,20 +1352,23 @@ def on_interim_transcript(self, ev: stt.SpeechEvent, *, speaking: bool | None) - # skip stt transcription if user_transcription is enabled on the realtime model return + transcript_text = ev.alternatives[0].text + self._session._user_input_transcribed( UserInputTranscribedEvent( language=ev.alternatives[0].language, - transcript=ev.alternatives[0].text, + transcript=transcript_text, is_final=False, speaker_id=ev.alternatives[0].speaker_id, ), ) - if ev.alternatives[0].text and self._turn_detection not in ( + if transcript_text and self._turn_detection not in ( "manual", "realtime_llm", ): - self._interrupt_by_audio_activity() + # Pass the transcript to enable soft input detection + self._interrupt_by_audio_activity(transcript=transcript_text) if ( speaking is False @@ -1276,10 +1383,12 @@ def on_final_transcript(self, ev: stt.SpeechEvent, *, speaking: bool | None = No # skip stt transcription if user_transcription is enabled on the realtime model return + transcript_text = ev.alternatives[0].text + self._session._user_input_transcribed( UserInputTranscribedEvent( language=ev.alternatives[0].language, - transcript=ev.alternatives[0].text, + transcript=transcript_text, is_final=True, speaker_id=ev.alternatives[0].speaker_id, ), @@ -1292,7 +1401,8 @@ def on_final_transcript(self, ev: stt.SpeechEvent, *, speaking: bool | None = No "manual", "realtime_llm", ): - self._interrupt_by_audio_activity() + # Pass the transcript to enable soft input detection + self._interrupt_by_audio_activity(transcript=transcript_text) if ( speaking is False diff --git a/livekit-agents/livekit/agents/voice/agent_session.py b/livekit-agents/livekit/agents/voice/agent_session.py index 628718a6b2..ff25f605d4 100644 --- a/livekit-agents/livekit/agents/voice/agent_session.py +++ b/livekit-agents/livekit/agents/voice/agent_session.py @@ -1,6 +1,7 @@ from __future__ import annotations import asyncio +import os import copy import time from collections.abc import AsyncIterable, Sequence @@ -72,6 +73,27 @@ class SessionConnectOptions: """Maximum number of consecutive unrecoverable errors from llm or tts.""" +DEFAULT_IGNORED_WORDS: list[str] = [ + "yeah", + "yep", + "yes", + "ok", + "okay", + "hmm", + "right", + "uh-huh", + "uh", + "mhm", + "mm", + "ah", + "aha", + "sure", + "got it", + "i see", +] +"""Default backchannel words that are ignored when the agent is speaking.""" + + @dataclass class AgentSessionOptions: allow_interruptions: bool @@ -88,6 +110,10 @@ class AgentSessionOptions: use_tts_aligned_transcript: bool | None preemptive_generation: bool tts_text_transforms: Sequence[TextTransforms] | None + ignored_words: Sequence[str] + """Words that are ignored when the agent is speaking (backchannel words).""" + fuzzy_match_threshold: int + """Similarity threshold (0-100) for fuzzy matching ignored words. Default 80.""" ivr_detection: bool @@ -157,6 +183,8 @@ def __init__( min_consecutive_speech_delay: float = 0.0, use_tts_aligned_transcript: NotGivenOr[bool] = NOT_GIVEN, tts_text_transforms: NotGivenOr[Sequence[TextTransforms] | None] = NOT_GIVEN, + ignored_words: NotGivenOr[Sequence[str]] = NOT_GIVEN, + fuzzy_match_threshold: int = 80, preemptive_generation: bool = False, ivr_detection: bool = False, conn_options: NotGivenOr[SessionConnectOptions] = NOT_GIVEN, @@ -236,6 +264,8 @@ def __init__( tts_text_transforms (Sequence[TextTransforms], optional): The transforms to apply to the tts input text, available built-in transforms: ``"filter_markdown"``, ``"filter_emoji"``. Set to ``None`` to disable. When NOT_GIVEN, all filters will be applied. + fuzzy_match_threshold (int): Similarity threshold (0-100) for fuzzy matching + ignored words. Higher values require closer matches. Default ``80``. preemptive_generation (bool): Whether to speculatively begin LLM and TTS requests before an end-of-turn is detected. When True, the agent sends inference calls as soon as a user @@ -284,6 +314,8 @@ def __init__( else DEFAULT_TTS_TEXT_TRANSFORMS ), preemptive_generation=preemptive_generation, + ignored_words=self._resolve_ignored_words(ignored_words), + fuzzy_match_threshold=fuzzy_match_threshold, ivr_detection=ivr_detection, use_tts_aligned_transcript=use_tts_aligned_transcript if is_given(use_tts_aligned_transcript) @@ -358,6 +390,21 @@ def __init__( # ivr activity self._ivr_activity: IVRActivity | None = None + @staticmethod + def _resolve_ignored_words( + ignored_words: NotGivenOr[Sequence[str]], + ) -> Sequence[str]: + """Resolve ignored words from parameter, environment variable, or default.""" + if is_given(ignored_words): + return ignored_words + + # Check environment variable (comma-separated list) + env_words = os.environ.get("LIVEKIT_AGENT_IGNORED_WORDS") + if env_words: + return [w.strip().lower() for w in env_words.split(",") if w.strip()] + + return DEFAULT_IGNORED_WORDS + def emit(self, event: EventTypes, arg: AgentEvent) -> None: # type: ignore self._recorded_events.append(arg) super().emit(event, arg) diff --git a/livekit-agents/pyproject.toml b/livekit-agents/pyproject.toml index 63ff41d0a7..0abc2e45a5 100644 --- a/livekit-agents/pyproject.toml +++ b/livekit-agents/pyproject.toml @@ -48,6 +48,7 @@ dependencies = [ "numpy>=1.26.0", "pydantic>=2.0,<3", "nest-asyncio>=1.6.0", + "rapidfuzz>=3.0.0", "opentelemetry-api>=1.34", "opentelemetry-sdk>=1.34.1", "opentelemetry-exporter-otlp>=1.34.1", diff --git a/tests/test_intelligent_interruption.py b/tests/test_intelligent_interruption.py new file mode 100644 index 0000000000..7f653f8947 --- /dev/null +++ b/tests/test_intelligent_interruption.py @@ -0,0 +1,395 @@ +""" +Tests for the intelligent interruption handling feature. + +This module tests the backchannel word filtering logic that prevents +short acknowledgement words from interrupting the agent while speaking. +""" + +import pytest + +from livekit.agents.voice.agent_session import DEFAULT_IGNORED_WORDS + + +class TestSoftInputDetection: + """Tests for the _is_soft_input method logic.""" + + def test_default_ignored_words_contains_common_backchannels(self): + """Verify default ignored words list contains common backchannel words.""" + expected_words = ["yeah", "ok", "hmm", "right", "uh-huh"] + for word in expected_words: + assert word in DEFAULT_IGNORED_WORDS, f"Expected '{word}' in DEFAULT_IGNORED_WORDS" + + def test_ignored_words_list_is_not_empty(self): + """Verify the default ignored words list is not empty.""" + assert len(DEFAULT_IGNORED_WORDS) > 0 + + def test_ignored_words_are_lowercase(self): + """Verify all default ignored words are lowercase for consistent matching.""" + for word in DEFAULT_IGNORED_WORDS: + assert word == word.lower(), f"Expected '{word}' to be lowercase" + + +class TestBackchannelWordMatching: + """Tests for backchannel word matching logic.""" + + @pytest.fixture + def ignored_words_set(self): + """Create a set of ignored words for testing.""" + return set(w.lower() for w in DEFAULT_IGNORED_WORDS) + + def _is_soft_input(self, text: str, ignored_words: set, threshold: int = 80) -> bool: + """ + Simplified version of the _is_soft_input logic for testing. + Mirrors the implementation in AgentActivity with fuzzy matching. + """ + import re + from rapidfuzz import fuzz, process + + if not text: + return True + + if not ignored_words: + return False + + normalized = text.lower().strip() + words = re.findall(r"[a-z0-9]+(?:[-'][a-z0-9]+)?", normalized) + + if not words: + return True + + # Use configurable fuzzy matching threshold + SIMILARITY_THRESHOLD = threshold + + # Check if ALL words are in the ignored list (exact or fuzzy match) + for word in words: + # Try exact match first (faster) + if word in ignored_words: + continue + + # Try fuzzy match with threshold using extractOne for efficiency + try: + best_match = process.extractOne( + word, + ignored_words, + scorer=fuzz.ratio, + score_cutoff=SIMILARITY_THRESHOLD + ) + + if not best_match: + return False # Found a word that doesn't match any ignored word + except Exception: + return False # On error, allow interruption + + return True # All words matched (exactly or fuzzily) + + def test_single_backchannel_word_is_soft(self, ignored_words_set): + """Test that single backchannel words are detected as soft input.""" + soft_inputs = ["yeah", "ok", "hmm", "right", "uh-huh", "mhm", "sure"] + for word in soft_inputs: + if word.replace("-", "") in ignored_words_set or word in ignored_words_set: + # Handle hyphenated words like "uh-huh" + assert self._is_soft_input(word, ignored_words_set), f"'{word}' should be soft input" + + def test_multiple_backchannel_words_is_soft(self, ignored_words_set): + """Test that multiple backchannel words together are still soft input.""" + # "yeah ok" - both are backchannel words + assert self._is_soft_input("yeah ok", ignored_words_set) + assert self._is_soft_input("ok sure", ignored_words_set) + assert self._is_soft_input("hmm yeah", ignored_words_set) + + def test_command_word_is_not_soft(self, ignored_words_set): + """Test that command words are NOT detected as soft input.""" + command_inputs = ["stop", "wait", "no", "hello", "start", "help"] + for word in command_inputs: + assert not self._is_soft_input(word, ignored_words_set), f"'{word}' should NOT be soft input" + + def test_mixed_input_is_not_soft(self, ignored_words_set): + """Test that mixed input (backchannel + command) is NOT soft input.""" + mixed_inputs = [ + "yeah wait", + "ok stop", + "hmm but wait", + "yeah okay but wait a second", + "sure but no", + ] + for text in mixed_inputs: + assert not self._is_soft_input(text, ignored_words_set), f"'{text}' should NOT be soft input" + + def test_empty_input_is_soft(self, ignored_words_set): + """Test that empty input is treated as soft.""" + assert self._is_soft_input("", ignored_words_set) + assert self._is_soft_input(" ", ignored_words_set) + + def test_case_insensitive_matching(self, ignored_words_set): + """Test that matching is case-insensitive.""" + assert self._is_soft_input("YEAH", ignored_words_set) + assert self._is_soft_input("Yeah", ignored_words_set) + assert self._is_soft_input("OK", ignored_words_set) + assert self._is_soft_input("Ok", ignored_words_set) + + def test_punctuation_is_ignored(self, ignored_words_set): + """Test that punctuation doesn't affect matching.""" + assert self._is_soft_input("yeah!", ignored_words_set) + assert self._is_soft_input("ok.", ignored_words_set) + assert self._is_soft_input("hmm...", ignored_words_set) + assert self._is_soft_input("yeah, ok", ignored_words_set) + + def test_question_with_command_is_not_soft(self, ignored_words_set): + """Test that questions with non-backchannel words are not soft.""" + assert not self._is_soft_input("what?", ignored_words_set) + assert not self._is_soft_input("can you repeat that?", ignored_words_set) + assert not self._is_soft_input("wait what?", ignored_words_set) + + +class TestFuzzyMatching: + """Tests for fuzzy matching of backchannel words.""" + + @pytest.fixture + def ignored_words_set(self): + """Create a set of ignored words for testing.""" + return set(w.lower() for w in DEFAULT_IGNORED_WORDS) + + def _is_soft_input(self, text: str, ignored_words: set) -> bool: + """ + Simplified version of the _is_soft_input logic for testing with fuzzy matching. + """ + import re + from rapidfuzz import fuzz + + if not text: + return True + + if not ignored_words: + return False + + normalized = text.lower().strip() + words = re.findall(r"[a-z0-9]+(?:[-'][a-z0-9]+)?", normalized) + + if not words: + return True + + SIMILARITY_THRESHOLD = 80 + + for word in words: + if word in ignored_words: + continue + + matched = False + for ignored_word in ignored_words: + similarity = fuzz.ratio(word, ignored_word) + if similarity >= SIMILARITY_THRESHOLD: + matched = True + break + + if not matched: + return False + + return True + + def test_fuzzy_match_typos(self, ignored_words_set): + """Test that common typos are matched fuzzily.""" + # Typos that should match with 80%+ similarity + fuzzy_inputs = [ + "yeah", # Exact match (baseline) + "yea", # Common shortening of "yeah" - 85.7% similarity + "yeahh", # Extra letter - 88.9% similarity + "okayy", # Extra letter for "okay" - 88.9% similarity + "hmmm", # Extended "hmm" (3 chars) - should match + ] + for text in fuzzy_inputs: + result = self._is_soft_input(text, ignored_words_set) + assert result, f"'{text}' should be matched fuzzily to an ignored word" + + def test_fuzzy_match_threshold_enforcement(self, ignored_words_set): + """Test that words below 80% threshold don't match.""" + # "hmmmm" vs "hmm" = 75% similarity (below threshold) + # Should NOT match + below_threshold = ["hmmmm", "yeahhhh"] + # Note: These might or might not match depending on other words in the ignored set + # We mainly verify they process correctly + + def test_fuzzy_match_phonetic_variations(self, ignored_words_set): + """Test phonetic variations are matched.""" + # Phonetic variations that STT might produce + phonetic_inputs = [ + "yep", # Should match "yep" in defaults + "yup", # Should match "yep" with 66% similarity (might not match at 80% threshold) + "uh huh", # Variation of "uh-huh" + "mhm", # Should match "mhm" exactly + ] + # Note: Some of these might not match at 80% threshold, adjust as needed + for text in phonetic_inputs: + # Just verify the function runs without error + self._is_soft_input(text, ignored_words_set) + + def test_fuzzy_no_match_for_very_different_words(self, ignored_words_set): + """Test that very different words don't match fuzzily.""" + # Words that should NOT match any ignored word + non_matching = [ + "stop", + "wait", + "hello", + "start", + "please", + ] + for word in non_matching: + assert not self._is_soft_input(word, ignored_words_set), \ + f"'{word}' should NOT fuzzy match any ignored word" + + def test_fuzzy_match_combined_with_exact(self, ignored_words_set): + """Test that fuzzy and exact matches work together.""" + # Mix of exact and fuzzy matches + assert self._is_soft_input("yeah yea", ignored_words_set) # Both should match + assert self._is_soft_input("ok okayy", ignored_words_set) # Both should match + + def test_fuzzy_threshold_boundary(self, ignored_words_set): + """Test behavior at similarity threshold boundary.""" + # Words at/near 80% threshold with "yeah" (4 chars) + # "yea" vs "yeah" = 75% similarity (3 matches out of 4) + # "yeaa" vs "yeah" = 75% similarity (3 matches, 1 substitution) + # "yeahh" vs "yeah" = 80% similarity (4 matches, 1 insertion) + + # This should match (>= 80%) + assert self._is_soft_input("yeahh", ignored_words_set) + + # Note: "yea" might or might not match depending on exact similarity calculation + # rapidfuzz might give different results, so we just verify it doesn't crash + + +class TestConfigurableThreshold: + """Tests for configurable fuzzy matching threshold.""" + + @pytest.fixture + def ignored_words_set(self): + """Create a set of ignored words for testing.""" + return set(w.lower() for w in DEFAULT_IGNORED_WORDS) + + def _is_soft_input(self, text: str, ignored_words: set, threshold: int = 80) -> bool: + """Simplified version of the _is_soft_input logic for testing.""" + import re + from rapidfuzz import process, fuzz + + if not text: + return True + + if not ignored_words: + return False + + normalized = text.lower().strip() + words = re.findall(r"[a-z0-9]+(?:[-'][a-z0-9]+)?", normalized) + + if not words: + return True + + SIMILARITY_THRESHOLD = threshold + + for word in words: + if word in ignored_words: + continue + + try: + best_match = process.extractOne( + word, + ignored_words, + scorer=fuzz.ratio, + score_cutoff=SIMILARITY_THRESHOLD + ) + + if not best_match: + return False + except Exception: + return False + + return True + + def test_higher_threshold_stricter(self, ignored_words_set): + """Test that higher threshold requires closer matches.""" + # "yea" vs "yeah" is around 85-90% similar + # Should match with threshold 80 + assert self._is_soft_input("yea", ignored_words_set, threshold=80) + + # But might not match with threshold 95 (very strict) + result_95 = self._is_soft_input("yea", ignored_words_set, threshold=95) + # We don't assert here because the exact score depends on rapidfuzz internals + # Just verify it doesn't crash + + def test_lower_threshold_more_lenient(self, ignored_words_set): + """Test that lower threshold allows more variations.""" + # With a very low threshold, more variations should match + assert self._is_soft_input("yeahh", ignored_words_set, threshold=50) + assert self._is_soft_input("okayy", ignored_words_set, threshold=50) + + def test_threshold_100_exact_match_only(self, ignored_words_set): + """Test that threshold 100 requires exact matches.""" + # Exact matches should still work + assert self._is_soft_input("yeah", ignored_words_set, threshold=100) + assert self._is_soft_input("ok", ignored_words_set, threshold=100) + + # Variations should NOT match + assert not self._is_soft_input("yeahh", ignored_words_set, threshold=100) + assert not self._is_soft_input("okayy", ignored_words_set, threshold=100) + + +class TestEdgeCases: + """Tests for edge cases in soft input detection.""" + + @pytest.fixture + def ignored_words_set(self): + """Create a set of ignored words for testing.""" + return set(w.lower() for w in DEFAULT_IGNORED_WORDS) + + def _is_soft_input(self, text: str, ignored_words: set) -> bool: + """Simplified version of the _is_soft_input logic for testing.""" + import re + from rapidfuzz import process, fuzz + + if not text: + return True + + if not ignored_words: + return False + + normalized = text.lower().strip() + words = re.findall(r"[a-z0-9]+(?:[-'][a-z0-9]+)?", normalized) + + if not words: + return True + + SIMILARITY_THRESHOLD = 80 + + for word in words: + if word in ignored_words: + continue + + best_match = process.extractOne( + word, + ignored_words, + scorer=fuzz.ratio, + score_cutoff=SIMILARITY_THRESHOLD + ) + + if not best_match: + return False + + return True + + def test_empty_string_is_soft(self, ignored_words_set): + """Test that empty string is considered soft input.""" + assert self._is_soft_input("", ignored_words_set) + + def test_whitespace_only_is_soft(self, ignored_words_set): + """Test that whitespace-only strings are considered soft input.""" + assert self._is_soft_input(" ", ignored_words_set) + assert self._is_soft_input("\t\n", ignored_words_set) + + def test_punctuation_only_is_soft(self, ignored_words_set): + """Test that punctuation-only strings are considered soft input.""" + assert self._is_soft_input("...", ignored_words_set) + assert self._is_soft_input("!!!", ignored_words_set) + assert self._is_soft_input("???", ignored_words_set) + + def test_unicode_handling(self, ignored_words_set): + """Test that non-ASCII characters don't break the system.""" + # These 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