feat: new parakeet v3 implementation

Multi languages, but less performant than v2. I tried on french, and it
was switching from english to french. Maybe some configuration is
required to get it right, but at the moment we cannot select any kind of
source translation from the UI, only target translation

CHANGELOG.md

@@ -1,5 +1,12 @@
 # Changelog
 
+## [0.7.1](https://github.com/Monadical-SAS/reflector/compare/v0.7.0...v0.7.1) (2025-08-21)
+
+
+### Bug Fixes
+
+* webvtt db null expectation mismatch ([#556](https://github.com/Monadical-SAS/reflector/issues/556)) ([e67ad1a](https://github.com/Monadical-SAS/reflector/commit/e67ad1a4a2054467bfeb1e0258fbac5868aaaf21))
+
 ## [0.7.0](https://github.com/Monadical-SAS/reflector/compare/v0.6.1...v0.7.0) (2025-08-21)
 
 

server/Dockerfile

@@ -27,4 +27,15 @@ COPY migrations /app/migrations
 COPY reflector /app/reflector
 WORKDIR /app
 
+# Create symlink for libgomp if it doesn't exist (for ARM64 compatibility)
+RUN if [ "$(uname -m)" = "aarch64" ] && [ ! -f /usr/lib/libgomp.so.1 ]; then \
+    LIBGOMP_PATH=$(find /app/.venv/lib -path "*/torch.libs/libgomp*.so.*" 2>/dev/null | head -n1); \
+    if [ -n "$LIBGOMP_PATH" ]; then \
+    ln -sf "$LIBGOMP_PATH" /usr/lib/libgomp.so.1; \
+    fi \
+    fi
+
+# Pre-check just to make sure the image will not fail
+RUN uv run python -c "import silero_vad.model"
+
 CMD ["./runserver.sh"]

server/gpu/modal_deployments/reflector_transcriber_parakeet_v3.py

@@ -0,0 +1,622 @@
+import logging
+import os
+import sys
+import threading
+import uuid
+from typing import Mapping, NewType
+from urllib.parse import urlparse
+
+import modal
+
+MODEL_NAME = "nvidia/parakeet-tdt-0.6b-v3"
+SUPPORTED_FILE_EXTENSIONS = ["mp3", "mp4", "mpeg", "mpga", "m4a", "wav", "webm"]
+SAMPLERATE = 16000
+UPLOADS_PATH = "/uploads"
+CACHE_PATH = "/cache"
+VAD_CONFIG = {
+    "max_segment_duration": 30.0,
+    "batch_max_files": 10,
+    "batch_max_duration": 5.0,
+    "min_segment_duration": 0.02,
+    "silence_padding": 0.5,
+    "window_size": 512,
+}
+
+ParakeetUniqFilename = NewType("ParakeetUniqFilename", str)
+AudioFileExtension = NewType("AudioFileExtension", str)
+
+app = modal.App("reflector-transcriber-parakeet-v3")
+
+# Volume for caching model weights
+model_cache = modal.Volume.from_name("parakeet-model-cache", create_if_missing=True)
+# Volume for temporary file uploads
+upload_volume = modal.Volume.from_name("parakeet-uploads", create_if_missing=True)
+
+image = (
+    modal.Image.from_registry(
+        "nvidia/cuda:12.8.0-cudnn-devel-ubuntu22.04", add_python="3.12"
+    )
+    .env(
+        {
+            "HF_HUB_ENABLE_HF_TRANSFER": "1",
+            "HF_HOME": "/cache",
+            "DEBIAN_FRONTEND": "noninteractive",
+            "CXX": "g++",
+            "CC": "g++",
+        }
+    )
+    .apt_install("ffmpeg")
+    .pip_install(
+        "hf_transfer==0.1.9",
+        "huggingface_hub[hf-xet]==0.31.2",
+        "nemo_toolkit[asr]==2.3.0",
+        "cuda-python==12.8.0",
+        "fastapi==0.115.12",
+        "numpy<2",
+        "librosa==0.10.1",
+        "requests",
+        "silero-vad==5.1.0",
+        "torch",
+    )
+    .entrypoint([])  # silence chatty logs by container on start
+)
+
+
+def detect_audio_format(url: str, headers: Mapping[str, str]) -> AudioFileExtension:
+    parsed_url = urlparse(url)
+    url_path = parsed_url.path
+
+    for ext in SUPPORTED_FILE_EXTENSIONS:
+        if url_path.lower().endswith(f".{ext}"):
+            return AudioFileExtension(ext)
+
+    content_type = headers.get("content-type", "").lower()
+    if "audio/mpeg" in content_type or "audio/mp3" in content_type:
+        return AudioFileExtension("mp3")
+    if "audio/wav" in content_type:
+        return AudioFileExtension("wav")
+    if "audio/mp4" in content_type:
+        return AudioFileExtension("mp4")
+
+    raise ValueError(
+        f"Unsupported audio format for URL: {url}. "
+        f"Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}"
+    )
+
+
+def download_audio_to_volume(
+    audio_file_url: str,
+) -> tuple[ParakeetUniqFilename, AudioFileExtension]:
+    import requests
+    from fastapi import HTTPException
+
+    response = requests.head(audio_file_url, allow_redirects=True)
+    if response.status_code == 404:
+        raise HTTPException(status_code=404, detail="Audio file not found")
+
+    response = requests.get(audio_file_url, allow_redirects=True)
+    response.raise_for_status()
+
+    audio_suffix = detect_audio_format(audio_file_url, response.headers)
+    unique_filename = ParakeetUniqFilename(f"{uuid.uuid4()}.{audio_suffix}")
+    file_path = f"{UPLOADS_PATH}/{unique_filename}"
+
+    with open(file_path, "wb") as f:
+        f.write(response.content)
+
+    upload_volume.commit()
+    return unique_filename, audio_suffix
+
+
+def pad_audio(audio_array, sample_rate: int = SAMPLERATE):
+    """Add 0.5 seconds of silence if audio is less than 500ms.
+
+    This is a workaround for a Parakeet bug where very short audio (<500ms) causes:
+    ValueError: `char_offsets`: [] and `processed_tokens`: [157, 834, 834, 841]
+    have to be of the same length
+
+    See: https://github.com/NVIDIA/NeMo/issues/8451
+    """
+    import numpy as np
+
+    audio_duration = len(audio_array) / sample_rate
+    if audio_duration < 0.5:
+        silence_samples = int(sample_rate * 0.5)
+        silence = np.zeros(silence_samples, dtype=np.float32)
+        return np.concatenate([audio_array, silence])
+    return audio_array
+
+
+@app.cls(
+    gpu="A10G",
+    timeout=600,
+    scaledown_window=300,
+    image=image,
+    volumes={CACHE_PATH: model_cache, UPLOADS_PATH: upload_volume},
+    enable_memory_snapshot=True,
+    experimental_options={"enable_gpu_snapshot": True},
+)
+@modal.concurrent(max_inputs=10)
+class TranscriberParakeetLive:
+    @modal.enter(snap=True)
+    def enter(self):
+        import nemo.collections.asr as nemo_asr
+
+        logging.getLogger("nemo_logger").setLevel(logging.CRITICAL)
+
+        self.lock = threading.Lock()
+        self.model = nemo_asr.models.ASRModel.from_pretrained(model_name=MODEL_NAME)
+        device = next(self.model.parameters()).device
+        print(f"Model is on device: {device}")
+
+    @modal.method()
+    def transcribe_segment(
+        self,
+        filename: str,
+    ):
+        import librosa
+
+        upload_volume.reload()
+
+        file_path = f"{UPLOADS_PATH}/{filename}"
+        if not os.path.exists(file_path):
+            raise FileNotFoundError(f"File not found: {file_path}")
+
+        audio_array, sample_rate = librosa.load(file_path, sr=SAMPLERATE, mono=True)
+        padded_audio = pad_audio(audio_array, sample_rate)
+
+        with self.lock:
+            with NoStdStreams():
+                (output,) = self.model.transcribe([padded_audio], timestamps=True)
+
+        text = output.text.strip()
+        words = [
+            {
+                "word": word_info["word"] + " ",
+                "start": round(word_info["start"], 2),
+                "end": round(word_info["end"], 2),
+            }
+            for word_info in output.timestamp["word"]
+        ]
+
+        return {"text": text, "words": words}
+
+    @modal.method()
+    def transcribe_batch(
+        self,
+        filenames: list[str],
+    ):
+        import librosa
+
+        upload_volume.reload()
+
+        results = []
+        audio_arrays = []
+
+        # Load all audio files with padding
+        for filename in filenames:
+            file_path = f"{UPLOADS_PATH}/{filename}"
+            if not os.path.exists(file_path):
+                raise FileNotFoundError(f"Batch file not found: {file_path}")
+
+            audio_array, sample_rate = librosa.load(file_path, sr=SAMPLERATE, mono=True)
+            padded_audio = pad_audio(audio_array, sample_rate)
+            audio_arrays.append(padded_audio)
+
+        with self.lock:
+            with NoStdStreams():
+                outputs = self.model.transcribe(audio_arrays, timestamps=True)
+
+        # Process results for each file
+        for i, (filename, output) in enumerate(zip(filenames, outputs)):
+            text = output.text.strip()
+
+            words = [
+                {
+                    "word": word_info["word"] + " ",
+                    "start": round(word_info["start"], 2),
+                    "end": round(word_info["end"], 2),
+                }
+                for word_info in output.timestamp["word"]
+            ]
+
+            results.append(
+                {
+                    "filename": filename,
+                    "text": text,
+                    "words": words,
+                }
+            )
+
+        return results
+
+
+# L40S class for file transcription (bigger files)
+@app.cls(
+    gpu="L40S",
+    timeout=900,
+    image=image,
+    volumes={CACHE_PATH: model_cache, UPLOADS_PATH: upload_volume},
+    enable_memory_snapshot=True,
+    experimental_options={"enable_gpu_snapshot": True},
+)
+class TranscriberParakeetFile:
+    @modal.enter(snap=True)
+    def enter(self):
+        import nemo.collections.asr as nemo_asr
+        import torch
+        from silero_vad import load_silero_vad
+
+        logging.getLogger("nemo_logger").setLevel(logging.CRITICAL)
+
+        self.model = nemo_asr.models.ASRModel.from_pretrained(model_name=MODEL_NAME)
+        device = next(self.model.parameters()).device
+        print(f"Model is on device: {device}")
+
+        torch.set_num_threads(1)
+        self.vad_model = load_silero_vad(onnx=False)
+        print("Silero VAD initialized")
+
+    @modal.method()
+    def transcribe_segment(
+        self,
+        filename: str,
+        timestamp_offset: float = 0.0,
+    ):
+        import librosa
+        import numpy as np
+        from silero_vad import VADIterator
+
+        def load_and_convert_audio(file_path):
+            audio_array, sample_rate = librosa.load(file_path, sr=SAMPLERATE, mono=True)
+            return audio_array
+
+        def vad_segment_generator(audio_array):
+            """Generate speech segments using VAD with start/end sample indices"""
+            vad_iterator = VADIterator(self.vad_model, sampling_rate=SAMPLERATE)
+            window_size = VAD_CONFIG["window_size"]
+            start = None
+
+            for i in range(0, len(audio_array), window_size):
+                chunk = audio_array[i : i + window_size]
+                if len(chunk) < window_size:
+                    chunk = np.pad(
+                        chunk, (0, window_size - len(chunk)), mode="constant"
+                    )
+
+                speech_dict = vad_iterator(chunk)
+                if not speech_dict:
+                    continue
+
+                if "start" in speech_dict:
+                    start = speech_dict["start"]
+                    continue
+
+                if "end" in speech_dict and start is not None:
+                    end = speech_dict["end"]
+                    start_time = start / float(SAMPLERATE)
+                    end_time = end / float(SAMPLERATE)
+
+                    # Extract the actual audio segment
+                    audio_segment = audio_array[start:end]
+
+                    yield (start_time, end_time, audio_segment)
+                    start = None
+
+            vad_iterator.reset_states()
+
+        def vad_segment_filter(segments):
+            """Filter VAD segments by duration and chunk large segments"""
+            min_dur = VAD_CONFIG["min_segment_duration"]
+            max_dur = VAD_CONFIG["max_segment_duration"]
+
+            for start_time, end_time, audio_segment in segments:
+                segment_duration = end_time - start_time
+
+                # Skip very small segments
+                if segment_duration < min_dur:
+                    continue
+
+                # If segment is within max duration, yield as-is
+                if segment_duration <= max_dur:
+                    yield (start_time, end_time, audio_segment)
+                    continue
+
+                # Chunk large segments into smaller pieces
+                chunk_samples = int(max_dur * SAMPLERATE)
+                current_start = start_time
+
+                for chunk_offset in range(0, len(audio_segment), chunk_samples):
+                    chunk_audio = audio_segment[
+                        chunk_offset : chunk_offset + chunk_samples
+                    ]
+                    if len(chunk_audio) == 0:
+                        break
+
+                    chunk_duration = len(chunk_audio) / float(SAMPLERATE)
+                    chunk_end = current_start + chunk_duration
+
+                    # Only yield chunks that meet minimum duration
+                    if chunk_duration >= min_dur:
+                        yield (current_start, chunk_end, chunk_audio)
+
+                    current_start = chunk_end
+
+        def batch_segments(segments, max_files=10, max_duration=5.0):
+            batch = []
+            batch_duration = 0.0
+
+            for start_time, end_time, audio_segment in segments:
+                segment_duration = end_time - start_time
+
+                if segment_duration < VAD_CONFIG["silence_padding"]:
+                    silence_samples = int(
+                        (VAD_CONFIG["silence_padding"] - segment_duration) * SAMPLERATE
+                    )
+                    padding = np.zeros(silence_samples, dtype=np.float32)
+                    audio_segment = np.concatenate([audio_segment, padding])
+                    segment_duration = VAD_CONFIG["silence_padding"]
+
+                batch.append((start_time, end_time, audio_segment))
+                batch_duration += segment_duration
+
+                if len(batch) >= max_files or batch_duration >= max_duration:
+                    yield batch
+                    batch = []
+                    batch_duration = 0.0
+
+            if batch:
+                yield batch
+
+        def transcribe_batch(model, audio_segments):
+            with NoStdStreams():
+                outputs = model.transcribe(audio_segments, timestamps=True)
+            return outputs
+
+        def emit_results(
+            results,
+            segments_info,
+            batch_index,
+            total_batches,
+        ):
+            """Yield transcribed text and word timings from model output, adjusting timestamps to absolute positions."""
+            for i, (output, (start_time, end_time, _)) in enumerate(
+                zip(results, segments_info)
+            ):
+                text = output.text.strip()
+                words = [
+                    {
+                        "word": word_info["word"],
+                        "start": round(
+                            word_info["start"] + start_time + timestamp_offset, 2
+                        ),
+                        "end": round(
+                            word_info["end"] + start_time + timestamp_offset, 2
+                        ),
+                    }
+                    for word_info in output.timestamp["word"]
+                ]
+
+                yield text, words
+
+        upload_volume.reload()
+
+        file_path = f"{UPLOADS_PATH}/{filename}"
+        if not os.path.exists(file_path):
+            raise FileNotFoundError(f"File not found: {file_path}")
+
+        audio_array = load_and_convert_audio(file_path)
+        total_duration = len(audio_array) / float(SAMPLERATE)
+        processed_duration = 0.0
+
+        all_text_parts = []
+        all_words = []
+
+        raw_segments = vad_segment_generator(audio_array)
+        filtered_segments = vad_segment_filter(raw_segments)
+        batches = batch_segments(
+            filtered_segments,
+            VAD_CONFIG["batch_max_files"],
+            VAD_CONFIG["batch_max_duration"],
+        )
+
+        batch_index = 0
+        total_batches = max(
+            1, int(total_duration / VAD_CONFIG["batch_max_duration"]) + 1
+        )
+
+        for batch in batches:
+            batch_index += 1
+            audio_segments = [seg[2] for seg in batch]
+            results = transcribe_batch(self.model, audio_segments)
+
+            for text, words in emit_results(
+                results,
+                batch,
+                batch_index,
+                total_batches,
+            ):
+                if not text:
+                    continue
+                all_text_parts.append(text)
+                all_words.extend(words)
+
+            processed_duration += sum(len(seg[2]) / float(SAMPLERATE) for seg in batch)
+
+        combined_text = " ".join(all_text_parts)
+        return {"text": combined_text, "words": all_words}
+
+
+@app.function(
+    scaledown_window=60,
+    timeout=600,
+    secrets=[
+        modal.Secret.from_name("reflector-gpu"),
+    ],
+    volumes={CACHE_PATH: model_cache, UPLOADS_PATH: upload_volume},
+    image=image,
+)
+@modal.concurrent(max_inputs=40)
+@modal.asgi_app()
+def web():
+    import os
+    import uuid
+
+    from fastapi import (
+        Body,
+        Depends,
+        FastAPI,
+        Form,
+        HTTPException,
+        UploadFile,
+        status,
+    )
+    from fastapi.security import OAuth2PasswordBearer
+    from pydantic import BaseModel
+
+    transcriber_live = TranscriberParakeetLive()
+    transcriber_file = TranscriberParakeetFile()
+
+    app = FastAPI()
+
+    oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
+
+    def apikey_auth(apikey: str = Depends(oauth2_scheme)):
+        if apikey == os.environ["REFLECTOR_GPU_APIKEY"]:
+            return
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Invalid API key",
+            headers={"WWW-Authenticate": "Bearer"},
+        )
+
+    class TranscriptResponse(BaseModel):
+        result: dict
+
+    @app.post("/v1/audio/transcriptions", dependencies=[Depends(apikey_auth)])
+    def transcribe(
+        file: UploadFile = None,
+        files: list[UploadFile] | None = None,
+        model: str = Form(MODEL_NAME),
+        language: str = Form("en"),
+        batch: bool = Form(False),
+    ):
+        # Parakeet only supports English
+        if language != "en":
+            raise HTTPException(
+                status_code=400,
+                detail=f"Parakeet model only supports English. Got language='{language}'",
+            )
+        # Handle both single file and multiple files
+        if not file and not files:
+            raise HTTPException(
+                status_code=400, detail="Either 'file' or 'files' parameter is required"
+            )
+        if batch and not files:
+            raise HTTPException(
+                status_code=400, detail="Batch transcription requires 'files'"
+            )
+
+        upload_files = [file] if file else files
+
+        # Upload files to volume
+        uploaded_filenames = []
+        for upload_file in upload_files:
+            audio_suffix = upload_file.filename.split(".")[-1]
+            assert audio_suffix in SUPPORTED_FILE_EXTENSIONS
+
+            # Generate unique filename
+            unique_filename = f"{uuid.uuid4()}.{audio_suffix}"
+            file_path = f"{UPLOADS_PATH}/{unique_filename}"
+
+            print(f"Writing file to: {file_path}")
+            with open(file_path, "wb") as f:
+                content = upload_file.file.read()
+                f.write(content)
+
+            uploaded_filenames.append(unique_filename)
+
+        upload_volume.commit()
+
+        try:
+            # Use A10G live transcriber for per-file transcription
+            if batch and len(upload_files) > 1:
+                # Use batch transcription
+                func = transcriber_live.transcribe_batch.spawn(
+                    filenames=uploaded_filenames,
+                )
+                results = func.get()
+                return {"results": results}
+
+            # Per-file transcription
+            results = []
+            for filename in uploaded_filenames:
+                func = transcriber_live.transcribe_segment.spawn(
+                    filename=filename,
+                )
+                result = func.get()
+                result["filename"] = filename
+                results.append(result)
+
+            return {"results": results} if len(results) > 1 else results[0]
+
+        finally:
+            for filename in uploaded_filenames:
+                try:
+                    file_path = f"{UPLOADS_PATH}/{filename}"
+                    print(f"Deleting file: {file_path}")
+                    os.remove(file_path)
+                except Exception as e:
+                    print(f"Error deleting {filename}: {e}")
+
+            upload_volume.commit()
+
+    @app.post("/v1/audio/transcriptions-from-url", dependencies=[Depends(apikey_auth)])
+    def transcribe_from_url(
+        audio_file_url: str = Body(
+            ..., description="URL of the audio file to transcribe"
+        ),
+        model: str = Body(MODEL_NAME),
+        language: str = Body("en", description="Language code (only 'en' supported)"),
+        timestamp_offset: float = Body(0.0),
+    ):
+        # Parakeet only supports English
+        if language != "en":
+            raise HTTPException(
+                status_code=400,
+                detail=f"Parakeet model only supports English. Got language='{language}'",
+            )
+        unique_filename, audio_suffix = download_audio_to_volume(audio_file_url)
+
+        try:
+            func = transcriber_file.transcribe_segment.spawn(
+                filename=unique_filename,
+                timestamp_offset=timestamp_offset,
+            )
+            result = func.get()
+            return result
+        finally:
+            try:
+                file_path = f"{UPLOADS_PATH}/{unique_filename}"
+                print(f"Deleting file: {file_path}")
+                os.remove(file_path)
+                upload_volume.commit()
+            except Exception as e:
+                print(f"Error cleaning up {unique_filename}: {e}")
+
+    return app
+
+
+class NoStdStreams:
+    def __init__(self):
+        self.devnull = open(os.devnull, "w")
+
+    def __enter__(self):
+        self._stdout, self._stderr = sys.stdout, sys.stderr
+        self._stdout.flush()
+        self._stderr.flush()
+        sys.stdout, sys.stderr = self.devnull, self.devnull
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        sys.stdout, sys.stderr = self._stdout, self._stderr
+        self.devnull.close()

server/reflector/db/search.py

@@ -45,7 +45,7 @@ SearchTotal = Annotated[
     SearchTotalBase, Field(description="Total number of search results")
 ]
 
-WEBVTT_SPEC_HEADER = "WEBVTT\n\n"
+WEBVTT_SPEC_HEADER = "WEBVTT"
 
 WebVTTContent = Annotated[
     str,
@@ -379,7 +379,13 @@ class SearchController:
             )
 
         if params.user_id:
-            base_query = base_query.where(transcripts.c.user_id == params.user_id)
+            base_query = base_query.where(
+                sqlalchemy.or_(
+                    transcripts.c.user_id == params.user_id, rooms.c.is_shared
+                )
+            )
+        else:
+            base_query = base_query.where(rooms.c.is_shared)
         if params.room_id:
             base_query = base_query.where(transcripts.c.room_id == params.room_id)
         if params.source_kind:

server/tests/test_search.py

@@ -74,11 +74,12 @@ async def test_empty_transcript_title_only_match():
             "share_mode": "private",
             "source_kind": "room",
             "webvtt": None,
+            "user_id": "test-user-1",
         }
 
         await get_database().execute(transcripts.insert().values(**test_data))
 
-        params = SearchParameters(query_text="empty")
+        params = SearchParameters(query_text="empty", user_id="test-user-1")
         results, total = await search_controller.search_transcripts(params)
 
         assert total >= 1
@@ -127,11 +128,12 @@ async def test_search_with_long_summary():
 
 00:00:00.000 --> 00:00:10.000
 Basic meeting content without special keywords.""",
+            "user_id": "test-user-2",
         }
 
         await get_database().execute(transcripts.insert().values(**test_data))
 
-        params = SearchParameters(query_text="quantum computing")
+        params = SearchParameters(query_text="quantum computing", user_id="test-user-2")
         results, total = await search_controller.search_transcripts(params)
 
         assert total >= 1
@@ -191,23 +193,26 @@ The search feature should support complex queries with ranking.
 
 00:00:30.000 --> 00:00:40.000
 We need to implement PostgreSQL tsvector for better performance.""",
+            "user_id": "test-user-3",
         }
 
         await get_database().execute(transcripts.insert().values(**test_data))
 
-        params = SearchParameters(query_text="planning")
+        params = SearchParameters(query_text="planning", user_id="test-user-3")
         results, total = await search_controller.search_transcripts(params)
         assert total >= 1
         found = any(r.id == test_id for r in results)
         assert found, "Should find test transcript by title word"
 
-        params = SearchParameters(query_text="tsvector")
+        params = SearchParameters(query_text="tsvector", user_id="test-user-3")
         results, total = await search_controller.search_transcripts(params)
         assert total >= 1
         found = any(r.id == test_id for r in results)
         assert found, "Should find test transcript by webvtt content"
 
-        params = SearchParameters(query_text="engineering planning")
+        params = SearchParameters(
+            query_text="engineering planning", user_id="test-user-3"
+        )
         results, total = await search_controller.search_transcripts(params)
         assert total >= 1
         found = any(r.id == test_id for r in results)
@@ -220,13 +225,17 @@ We need to implement PostgreSQL tsvector for better performance.""",
             assert test_result.duration == 1800.0
             assert 0 <= test_result.rank <= 1, "Rank should be normalized to 0-1"
 
-        params = SearchParameters(query_text="tsvector OR nosuchword")
+        params = SearchParameters(
+            query_text="tsvector OR nosuchword", user_id="test-user-3"
+        )
         results, total = await search_controller.search_transcripts(params)
         assert total >= 1
         found = any(r.id == test_id for r in results)
         assert found, "Should find test transcript with OR query"
 
-        params = SearchParameters(query_text='"full-text search"')
+        params = SearchParameters(
+            query_text='"full-text search"', user_id="test-user-3"
+        )
         results, total = await search_controller.search_transcripts(params)
         assert total >= 1
         found = any(r.id == test_id for r in results)

server/tests/test_search_long_summary.py

@@ -54,12 +54,13 @@ The robotics project is making good progress.
 
 00:00:20.000 --> 00:00:30.000
 We need to consider various implementation approaches.""",
+            "user_id": "test-user-priority",
         }
 
         await get_database().execute(transcripts.insert().values(**test_data))
 
         # Search for "robotics" which appears in both long_summary and webvtt
-        params = SearchParameters(query_text="robotics")
+        params = SearchParameters(query_text="robotics", user_id="test-user-priority")
         results, total = await search_controller.search_transcripts(params)
 
         assert total >= 1
@@ -131,12 +132,13 @@ Team meeting about general project updates.
 
 00:00:10.000 --> 00:00:20.000
 Discussion of timeline and deliverables.""",
+            "user_id": "test-user-long",
         }
 
         await get_database().execute(transcripts.insert().values(**test_data))
 
         # Search for terms only in long_summary
-        params = SearchParameters(query_text="cryptocurrency")
+        params = SearchParameters(query_text="cryptocurrency", user_id="test-user-long")
         results, total = await search_controller.search_transcripts(params)
 
         found = any(r.id == test_id for r in results)
@@ -151,7 +153,7 @@ Discussion of timeline and deliverables.""",
         assert "cryptocurrency" in snippet, "Snippet should contain the search term"
 
         # Search for "yield farming" - a more specific term
-        params2 = SearchParameters(query_text="yield farming")
+        params2 = SearchParameters(query_text="yield farming", user_id="test-user-long")
         results2, total2 = await search_controller.search_transcripts(params2)
 
         found2 = any(r.id == test_id for r in results2)