i want to save voice conversation both the agent and the users to a MongoDB , how do I do that?

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python client

Thanks — I’m responding to your request about saving both agent and user audio from a Python client to MongoDB. Below is a practical, step-by-step guide with sample Python code you can adapt. I’ll assume you’re using the LiveKit Python RTC client to subscribe to audio tracks; if you’re using a different setup, tell me and I’ll adjust. High-level flow 1. Subscribe to remote audio tracks for both participants. 2. Capture audio frames from each track using an audio stream helper. 3. Convert frames into a standard audio file (WAV or MP3). 4. Store the audio bytes in MongoDB using GridFS and save metadata in a collection. 5. Finalize when the track is unpublished or the participant leaves. Important note: For files larger than 16 MB, use GridFS so MongoDB stores audio in chunks. Sample implementation (conceptual) • This example shows: ◦ Listening for

track_subscribed

and

track_unsubscribed

events. ◦ Buffering frames per participant/track. ◦ Converting buffered frames to WAV bytes. ◦ Writing to MongoDB GridFS and saving metadata. 1) Install dependencies • livekit-client (Python SDK) • pymongo • gridfs • numpy + soundfile or wave (for WAV encoding) • optionally pydub + ffmpeg if you want MP3 Example pip install:

pip install livekit-client pymongo soundfile numpy
# If you want MP3 support:
pip install pydub
# ensure ffmpeg is installed on the system for pydub MP3 encoding

2) Capture, encode, and save (example)

import asyncio
import io
import datetime
from pymongo import MongoClient
import gridfs
import soundfile as sf
import numpy as np
from livekit import Room, TrackKind, RemoteAudioTrack

# Connect to MongoDB and GridFS
mongo_client = MongoClient("<mongodb://localhost:27017/>")
db = mongo_client["voice_conversations"]
fs = gridfs.GridFS(db)
conversations = db["conversations_metadata"]

# In-memory buffers per track
track_buffers = {}  # key: track.sid or (participant.identity, track.sid), value: list of numpy arrays

# Helper to convert frames list to WAV bytes
def frames_to_wav_bytes(frames, sample_rate=48000, channels=1):
    """
    frames: list of numpy arrays (dtype=float32 or int16) representing PCM samples
    sample_rate: sample rate of frames
    channels: number of channels
    Returns bytes of a WAV file.
    """
    # Concatenate frames
    if not frames:
        return None
    audio = np.concatenate(frames, axis=0)
    # Ensure float32 -> write as PCM 16
    if audio.dtype == np.float32 or audio.dtype == np.float64:
        audio_int16 = (audio * 32767).astype(np.int16)
    else:
        audio_int16 = audio.astype(np.int16)

    buf = io.BytesIO()
    # soundfile can write to file-like objects
    sf.write(buf, audio_int16, sample_rate, subtype='PCM_16')
    buf.seek(0)
    return buf.read()

# Event handlers
async def on_track_subscribed(track: RemoteAudioTrack, publication, participant):
    # Only audio tracks
    if track.kind != TrackKind.AUDIO:
        return

    key = (participant.identity, track.sid)
    track_buffers[key] = []

    # Use the LiveKit audio stream helper if available; otherwise assume track provides frames via async iterator
    async for frame in track.stream():
        # The exact frame type depends on SDK; transform to numpy PCM float32 in range [-1, 1] or int16
        # Example assumes frame.to_numpy() exists — adapt to actual SDK API
        pcm = frame.to_numpy()  # adapt this call to real API
        track_buffers[key].append(pcm)

async def on_track_unsubscribed(publication, participant):
    key = (participant.identity, publication.track_sid)
    frames = track_buffers.pop(key, None)
    if not frames:
        return

    wav_bytes = frames_to_wav_bytes(frames, sample_rate=48000, channels=1)
    if not wav_bytes:
        return

    filename = f"{participant.identity}_{publication.track_sid}_{datetime.datetime.utcnow().isoformat()}.wav"
    file_id = fs.put(wav_bytes, filename=filename, metadata={
        "participant": participant.identity,
        "track_sid": publication.track_sid,
        "timestamp": datetime.datetime.utcnow()
    })

    # Save metadata document linking to file
    conversations.insert_one({
        "participant": participant.identity,
        "track_sid": publication.track_sid,
        "file_id": file_id,
        "filename": filename,
        "created_at": datetime.datetime.utcnow()
    })

# Room setup and event wiring (simplified)
async def main():
    room = Room()
    # connect to your LiveKit server/room (provide url and token per your flow)
    await room.connect("<wss://your-livekit-host>", "ACCESS_TOKEN")

    room.on("track_subscribed", on_track_subscribed)
    room.on("track_unsubscribed", on_track_unsubscribed)

    # Keep running
    await asyncio.Event().wait()

if __name__ == "__main__":
    asyncio.run(main())

Notes and adaptations • The exact frame API depends on the Python LiveKit SDK version. If frames are provided as raw bytes or a custom object, adapt

frame.to_numpy()

accordingly. • If the SDK exposes an

AudioStream

helper with an async iterator of frames, use that. The goal is to collect PCM samples (int16 or float32). • For less memory pressure, consider streaming to GridFS incrementally: ◦ Create a GridFS upload stream (

fs.new_file()

or

fs.put

with chunks) and write chunks as frames arrive, instead of buffering the entire conversation in memory. • If you want MP3 rather than WAV, use

pydub

to convert WAV bytes to MP3 and store MP3 bytes to GridFS; MP3 reduces storage but requires CPU to encode. • Save metadata (participant identities, room, start/end times, agent vs user roles) to a separate collection to ease retrieval and searching. When to finalize/save • Use

track_unsubscribed

,

participant_left

, or

on_track_muted

events to decide when to flush and write audio for a track. • If you segment recordings (e.g., per utterance or every N seconds), you can periodically encode and append/write chunks to GridFS. Performance and storage tips • Compress to MP3 if storage/transfer is a concern. • Stream to GridFS instead of buffering large files. • Record at an appropriate sample rate (48 kHz is common for WebRTC/LiveKit, but you can downsample to 16 kHz for voice-only use to save space). • Keep metadata minimal but include participant ids, timestamps, room id, and file_id. If you want, I can: • Provide a version that streams directly to GridFS incrementally (no full in-memory buffer). • Provide adapter code for the exact LiveKit Python SDK frame API if you share which SDK version you’re using or paste a sample frame object. • Show how to combine agent and user audio into a single mixed file or store them as separate files. Which of those would you like next?

what about he server version

Thanks — I’m responding to your question about the server version for saving both agent and user audio to MongoDB. I’ll cover the realistic server-side approaches you can use with LiveKit and how to persist recordings into MongoDB (GridFS). First, one quick clarification so I tailor the answer: Which server approach do you want? • Use LiveKit Universal Egress (server-side recording/export of whole session to files, then upload to MongoDB), or • A custom server that subscribes to participant tracks in real time and streams audio directly into MongoDB (no intermediate file), or • Mix-and-record (single mixed audio file) vs per-participant separate files? Tell me which of those you prefer and your server runtime (Python version, framework) and I’ll give sample code for that flow. Below I describe both major options and practical steps you can follow now. High-level options (server-side) • Option A — Egress then store: Let LiveKit produce recording files (egress) — e.g., WAV/MP3/MP4 saved to a temporary location or cloud storage — then have your server fetch those files and save into MongoDB GridFS (recommended for reliability and low implementation complexity). ◦ Pros: Simpler, battle-tested recording via LiveKit egress; less custom streaming code; easier to support high scale. ◦ Cons: Slight delay (post-recording), requires transferring files to your server to insert into MongoDB or saving directly from cloud storage. • Option B — Server subscription + direct streaming to MongoDB: Run a server process that joins the LiveKit room as a participant, subscribes to audio tracks (agent and user), captures PCM frames in real time, and writes encoded audio chunks directly into MongoDB GridFS (either incrementally streaming into a GridFS file or appending small files per segment). ◦ Pros: Lower latency storage, more real-time processing (transcription, analytics). ◦ Cons: More complex: you must handle audio encoding, concurrency, buffering, and track lifecycle. Also requires more server resources. Common storage detail for both: • Use GridFS for binary audio storage (files >16MB). • Save metadata (participant identity, room id, start/end timestamps, agent vs user role, track ids, file_id) in a separate MongoDB collection for quick lookup. • Prefer a compressed format (MP3/Opus) for storage efficiency; WAV is simplest for raw PCM. Practical flows and considerations Option A — LiveKit Egress then save to MongoDB 1. Configure and start egress to produce recordings (WAV/MP4/MP3) for the whole room or per-track. (Egress can produce single mixed files or individual per-participant files.) 2. When egress completes, either: ◦ Download the recorded file to your server, then insert into GridFS; or ◦ If egress writes to cloud storage (S3/GCS), have your server fetch the object and push bytes to GridFS, or run a cloud function to transfer directly into MongoDB. 1. Store metadata in a collection linking to the GridFS file id, plus participant/room info and timestamps. Key steps for implementation (A): 1. Trigger egress (via LiveKit server API) to record the session. Configure single/multi-file output depending on whether you want mixed or per-participant files. 2. After egress completes, obtain the file URL or path. 3. Download or stream the file into GridFS using PyMongo GridFS APIs: ◦ Use gridfs.GridFS(db).put(file_bytes, filename=..., metadata={...}) for smaller files. ◦ For large files, open a GridFS write stream and write chunks as you download. Option B — Server participant subscribing and streaming into GridFS 1. Create a server process that connects to the LiveKit room with a server-generated access token and a participant identity such as "recorder". 2. Subscribe to incoming audio tracks (both agent and user). Listen for track_subscribed and received frames. 3. Convert frames to a consistent PCM format (e.g., 48kHz, mono or stereo as you prefer). 4. Encode frames periodically into a chosen container (WAV or MP3). To avoid buffering huge in-memory arrays: ◦ Create a GridFS file stream and write audio chunks incrementally, or ◦ Periodically produce small files (e.g., 30s chunks) and store each chunk in GridFS with timestamps and a session id. 1. Finalize file(s) when the track ends (track_unsubscribed/participant_left) and write metadata. Practical implementation notes for Option B (server Python) • LiveKit Python client: use the server-side SDK or connect with the same RTC client library to join as a participant. Use SDK events to get audio frames (the exact API depends on SDK release). • Audio frame handling: you’ll likely receive PCM frames; convert these to numpy arrays or raw bytes and then: ◦ Use soundfile, wave, or similar to produce WAV chunks; or ◦ Pipe PCM into ffmpeg subprocess for encoding to MP3/Opus on the fly and write the encoded bytes into GridFS. • Streaming into GridFS: ◦ Use gridfs.GridFSBucket.open_upload_stream(filename, metadata={...}) or GridFS().new_file() to open a write stream and call .write(bytes) for each chunk, then .close() when finished. • Chunking strategy: ◦ For reliability and searchability, create small chunk files (e.g., 10–60s each) with metadata linking order. This avoids long-running uploads and simplifies retries. Small code sketches (conceptual, not copy-paste ready) • GridFS incremental upload (PyMongo)

from pymongo import MongoClient
import gridfs

client = MongoClient("<mongodb://localhost:27017/>")
db = client["voice_conversations"]
fs = gridfs.GridFSBucket(db)

upload_stream = fs.open_upload_stream("session123_participantA.wav", metadata={"participant":"A","room":"room123"})
# when you have encoded bytes chunk:
upload_stream.write(encoded_chunk_bytes)
# repeat until done
upload_stream.close()
# file id available at upload_stream._file_id (or returned)

• Using ffmpeg to encode stream to MP3 while piping PCM frames: ◦ Spawn ffmpeg subprocess with stdin pipe, write raw PCM into stdin, read encoded MP3 from stdout, and write that to GridFS incrementally. When to finalize/save • Finalize on track_unsubscribed / participant_left events. • For egress-based flow, finalize when egress completes. Which approach should you pick? • If you want reliability and less engineering effort: use Egress (Option A) and then store egress files into GridFS. • If you need near-real-time storage, per-utterance processing, or want to do live analytics/transcription before storing: use Option B. Next steps I can provide for you • If you prefer Egress: I’ll give a Python example that polls/receives egress completion, downloads the file, and stores it into GridFS and metadata collection. • If you prefer server subscription and streaming: I’ll provide a more detailed Python sample that subscribes to tracks, encodes chunks with ffmpeg, and streams into GridFS incrementally (including sample code to manage track lifecycle and metadata). • If you tell me whether you want mixed vs per-participant files and whether you want WAV or MP3/Opus, I’ll tailor the example. Which server approach and audio format do you want me to prepare detailed code for? Sources: Why is there extra audio at the end of my recordings of an agent session? |... | Transcriber | LiveKit Documentation | Universal Egress

lets use the client option seems very easy .. and also audio the audio and the trancitption ot that audio sabed to mong db ,so we have truto , user and also the trancritpion and audio where is more easier to implement that the frontend or the agnt python side ?

see my python code
async def entrypoint(ctx: JobContext):  
    # Set log context fields early  
    ctx.log_context_fields = {  
        "room": ctx.room.name,  
    }  
    
    # IMPORTANT: Get participant info first to know student details
    await ctx.connect()
    participant = await ctx.wait_for_participant()
    user_id = participant.identity  
    student_name = participant.name
    
    <http://logger.info|logger.info>("=" * 80)
    <http://logger.info|logger.info>(f":mortar_board: VOICE AGENT SESSION STARTED")
    <http://logger.info|logger.info>(f"Participant: {student_name} (ID: {user_id})")
    
    # Get subject and topic from participant metadata (sent by frontend in token)
    subject = None
    topic = None
    subject_id = None
    auth_token = None # Extract auth token for reporting
    prompt_text = None
    prompt_file_used = "default_fallback"
    
    if participant.metadata:
        try:
            metadata = json.loads(participant.metadata)
            subject = metadata.get("subject")
            topic = metadata.get("topic")
            subject_id = metadata.get("subjectId")
            auth_token = metadata.get("authToken")
            
            <http://logger.info|logger.info>(f":books: Subject: {subject or 'Not specified'}")
            <http://logger.info|logger.info>(f":id: Subject ID: {subject_id or 'Not specified'}")
            <http://logger.info|logger.info>(f":book: Topic: {topic or 'Not specified (will use core)'}")
            
            # Load prompt from file BEFORE creating agent
            if subject:
                prompt_text = await load_prompt_from_file(subject, topic)
                if prompt_text:
                    if topic:
                        prompt_file_used = f"prompts/{subject}/{topic}.txt"
                        <http://logger.info|logger.info>(f":white_check_mark: USING TOPIC PROMPT: prompts/{subject}/{topic}.txt")
                    else:
                        prompt_file_used = f"prompts/{subject}/core_{subject}_tutor.txt"
                        <http://logger.info|logger.info>(f":white_check_mark: USING CORE PROMPT: prompts/{subject}/core_{subject}_tutor.txt")
                    <http://logger.info|logger.info>(f":memo: Prompt preview (first 200 chars): {prompt_text[:200]}...")
                else:
                    logger.warning(f":warning:  No prompt file found for {subject}/{topic}, using fallback")
            else:
                logger.warning(f":warning:  No subject specified, using default prompt")
        except Exception as e:
            logger.error(f":x: Error parsing participant metadata: {e}")
    else:
        logger.warning(":warning:  No participant metadata found, using default prompt")
    
    <http://logger.info|logger.info>("=" * 80)
      
    # Load chat history
    temp_agent = WAECExamAgent(user_id=user_id, student_name=student_name)  
    chat_ctx = await temp_agent.load_chat_history()  
      
    agent = WAECExamAgent(
        user_id=user_id, 
        student_name=student_name, 
        chat_ctx=chat_ctx, 
        instructions_text=prompt_text
    )
    
    <http://logger.info|logger.info>("=" * 80)
    <http://logger.info|logger.info>(f":robot_face: AGENT INITIALIZED")
    <http://logger.info|logger.info>(f"Prompt file used: {prompt_file_used}")
    <http://logger.info|logger.info>(f"Instructions loaded: {'YES :white_check_mark:' if prompt_text else 'NO - Using fallback :warning:'}")
    if prompt_text:
        <http://logger.info|logger.info>(f"Instructions length: {len(prompt_text)} characters")
    <http://logger.info|logger.info>("=" * 80)  
      
      
    session = AgentSession(  
        vad=ctx.proc.userdata["vad"],  
        llm=openai.LLM(model="gpt-4o-mini", api_key=os.getenv("OPENAI_API_KEY") ),
        stt=openai.STT(model="whisper-1"),  
        tts=openai.TTS(  
            base_url="<https://prepai-tts.publicaai.com/v1>",  
            model="chatterbox-trubo", 
            voice="cloned-voice"  
        ),  
    )  
    
      
    usage_collector = metrics.UsageCollector()  
    @session.on("metrics_collected")  
    def _on_metrics_collected(ev: MetricsCollectedEvent):  
        metrics.log_metrics(ev.metrics)  
        usage_collector.collect(ev.metrics)  
      
    # Start session with standard options
    await session.start(  
        agent=agent,  
        room=ctx.room,
        # Using RoomInputOptions for text/audio input
        room_input_options=RoomInputOptions(
            text_enabled=True,
            audio_enabled=True,
           
        ),
        room_output_options=RoomOutputOptions(
            transcription_enabled=True,
            audio_enabled=True
        )
    )  
      
    ctx.log_context_fields["user_id"] = user_id  
    
    # Assign session to agent to allow manual triggering
    agent.session = session
      
    <http://logger.info|logger.info>(f"Agent initialized with prompt for subject: {subject or 'default'}, topic: {topic or 'core'}")  
    
    # --- Vision Logic Restoration ---
    def handle_vision_message(reader, participant_identity):
        """Handle vision messages sent via text stream"""
        async def _process():
            try:
                # Use read_all() as per correct API
                message_text = await reader.read_all()
                <http://logger.info|logger.info>(f"cam:camera_with_flash: Received vision text message from {participant_identity}")
                
                try:
                    message_data = json.loads(message_text)
                    await agent.process_vision_message(message_data)
                except json.JSONDecodeError:
                    logger.warning(f"Received non-JSON vision message: {message_text[:100]}...")
            except Exception as e:
                logger.error(f"Error in vision message handler: {str(e)}", exc_info=True)
        
        asyncio.create_task(_process())

    # Register the handler
    room = ctx.room
    room.register_text_stream_handler('lk.chat.vision', handle_vision_message)
    <http://logger.info|logger.info>(":white_check_mark: Registered text stream handler for 'lk.chat.vision'")
    # --------------------------------
      
    async def log_usage():  
        summary = usage_collector.get_summary()  
        <http://logger.info|logger.info>(f"Usage summary: {summary}")
        
        # Save usage directly to MongoDB (No external API call needed)
        try:
            # Calculate total tokens (LLM + STT + TTS)
            # We combine all metrics into a single "usage unit" for simplicity, 
            # or you might want to store them separately if the DB schema allowed.
            # For now, we sum: LLM Tokens + TTS Characters + STT Seconds
            llm_total = summary.llm_prompt_tokens + summary.llm_completion_tokens
            tts_total = summary.tts_characters_count
            stt_total = int(summary.stt_audio_duration)
            
            total_usage_units = llm_total + tts_total + stt_total
            
            # Prepare payload for external API
            # Note: studentId is omitted as the server uses the auth token for identification
            external_request_id = str(uuid.uuid4())
            payload = {
                "studentId": user_id,
                "tokensUsed": total_usage_units,
                "requestId": external_request_id,
                "token": auth_token,
                "name": student_name,
                "subject": subject,
                "topic": topic
             
            }
            
            # Send usage to external API
            external_api_url = os.getenv("BACKEND_USAGE_URL")
            mask_token = f"{auth_token[:10]}...{auth_token[-10:]}" if auth_token else "NONE"
            <http://logger.info|logger.info>(f":outbox_tray: Reporting usage to external API: {external_api_url}")
            <http://logger.info|logger.info>(f":package: Payload: studentId={user_id}, tokens={total_usage_units}, token={mask_token}")
            
            # Set up headers with authorization
            headers = {"Content-Type": "application/json"}
            if auth_token:
                headers["Authorization"] = f"Bearer {auth_token}"
            else:
                logger.warning(":warning: No auth token available for external API reporting")
            
            async with aiohttp.ClientSession() as session:
                try:
                    async with <http://session.post|session.post>(
                        external_api_url, 
                        json=payload, 
                        headers=headers,
                        timeout=10
                    ) as response:
                        if response.status in [200, 201]:
                            <http://logger.info|logger.info>(f":white_check_mark: Successfully reported usage to external API. Status: {response.status}")
                        else:
                            resp_text = await response.text()
                            logger.warning(f":warning: External API returned error status {response.status}: {resp_text}")
                            <http://logger.info|logger.info>(f":bulb: Hint: If 403, check if the token/subjectId match an active allocation on the server.")
                except Exception as api_err:
                    logger.error(f":x: Failed to reach external API: {api_err}")

            # Keep existing MongoDB storage as backup/local log
            store_token_usage(
                student_id=user_id,
                tokens=total_usage_units,
                request_id=external_request_id,
                model_name="voice-agent-combined",
                subject_id=subject_id,
                session_type="voice_tutoring"
            )
            
        except Exception as e:
            logger.error(f":x: Error in token usage reporting: {e}", exc_info=True)
  
  
    async def save_conversation_on_shutdown():  
        try:  
            await agent.save_chat_history(agent.chat_ctx)  
            <http://logger.info|logger.info>(f"Conversation saved successfully for user: {user_id}")  
        except Exception as e:  
            logger.error(f"Error saving conversation: {str(e)}")  
  
    async def delete_room():  
        try:  
            async with api.LiveKitAPI(  
                os.getenv("LIVEKIT_URL"),  
                os.getenv("LIVEKIT_API_KEY"),  
                os.getenv("LIVEKIT_API_SECRET")  
            ) as api_client:  
                await api_client.room.delete_room(api.DeleteRoomRequest(  
                    room=ctx.room.name,  
                ))  
                <http://logger.info|logger.info>("Room deleted successfully")  
        except Exception as e:  
            logger.error(f"Error deleting room: {str(e)}")  
  
    ctx.add_shutdown_callback(save_conversation_on_shutdown)  
    ctx.add_shutdown_callback(log_usage)  
    ctx.add_shutdown_callback(delete_room)  
      
    # Handle data channel messages for interruptions  
    def on_data_received(data: rtc.DataPacket):  
        async def _process_data():  
            try:  
                if data.topic == "lk-rpc-request":  
                    payload = json.loads(data.data.decode('utf-8'))  
                    if payload.get("method") == "start_turn":  
                        <http://logger.info|logger.info>("Interrupting agent via data channel")  
                        session.interrupt()  
  
                        await ctx.room.local_participant.publish_data(  
                            json.dumps({"status": "interrupted"}).encode(),  
                            topic="agent_control"  
                        )  
            except Exception as e:  
                logger.error(f"Data handling error: {str(e)}", exc_info=True)  
  
        asyncio.create_task(_process_data())  
  
    ctx.room.on("data_received", on_data_received)  
  
if name == "main":  
    cli.run_app(WorkerOptions(entrypoint_fnc=entrypoint, prewarm_fnc=prewarm))

Thanks — I’m responding to your message about saving both audio and its transcription to MongoDB, and whether to implement this on the frontend (client) or on your Python agent backend. You shared your Python agent code — great context. Below I’ll: • Give a short recommendation, • Provide a clear client-side (browser) flow you asked for with practical code sketches (JS client + a small Python server endpoint) that store audio in GridFS and transcripts in a collection, • Note backend advantages and when you might switch to server-side, • Ask one quick question to tailor the next snippet. Short recommendation • Client-side is easiest to implement quickly: use the browser to record audio (MediaRecorder or LiveKit track capture), send audio chunks to your server, and send transcript text (either from client STT or server STT) alongside. This is simple to prototype. • However, the Python agent/server side is more reliable, secure, and easier to correlate with server STT (you already have STT running in your agent). If you need production reliability, do it on the agent side later. For now, client-side is fine to get started. Client-side flow (what to implement) 1. On the browser: ◦ Capture audio from the participant (getRemoteAudioTrack or local mic) with MediaRecorder or LiveKit track APIs. ◦ Optionally chunk audio into small segments (e.g., 5–30s). ◦ Send each audio chunk to your server via fetch POST (multipart/form-data or binary). ◦ Send the transcript text for that chunk (if you run STT in-browser or get interim transcripts from server) in the same request or a separate request. ◦ Include metadata: user_id, participant name, room id, timestamps, and a session_id. 1. On your Python server: ◦ Provide an endpoint that accepts audio chunks + metadata and stores audio bytes in MongoDB GridFS and the transcription/metadata in a collection. ◦ Use chunking or GridFS streaming to avoid buffering large files in memory. ◦ Save a reference from the transcript document to the GridFS file id so you can retrieve audio + transcript together. Client-side example (JavaScript, conceptual) • Use MediaRecorder for simplicity (record local mic). If you want to record remote audio tracks, you can route the audio track to an AudioContext and capture it similarly or use LiveKit’s APIs for track frames.

// Example: record microphone using MediaRecorder and send chunks to server
const stream = await navigator.mediaDevices.getUserMedia({ audio: true });
const recorder = new MediaRecorder(stream, { mimeType: 'audio/webm' }); // or 'audio/wav' via processing
const sessionId = 'session-123'; // generate or get from server
const userId = 'user-abc';
let partIndex = 0;

recorder.ondataavailable = async (e) => {
  if (!e.data || e.data.size === 0) return;
  const form = new FormData();
  form.append('audio', e.data, `chunk-${partIndex}.webm`);
  form.append('session_id', sessionId);
  form.append('user_id', userId);
  form.append('part_index', partIndex);
  form.append('timestamp', new Date().toISOString());

  // If you have a client-side transcript for this chunk, include it:
  // form.append('transcript', transcriptText);

  await fetch('/api/upload_audio_chunk', {
    method: 'POST',
    body: form,
    credentials: 'include'
  });

  partIndex += 1;
};

recorder.start(5000); // timeslice: get dataavailable every 5s
// stop later: recorder.stop()

If you use LiveKit audio tracks rather than local mic, either: • Use the browser MediaStream from getUserMedia if participants supply mic audio, or • Use the LiveKit API to capture a remote/local track into an AudioContext and then record via ScriptProcessor/AudioWorklet and encode to WAV/PCM (more code, but feasible). Transcription on the client: • If you want transcripts produced on the client, use a browser-based STT (if available), or send audio chunks to your server which runs STT (recommended if you already have server STT). • Alternatively, you can send audio chunks to your server and let the agent’s

stt=openai.STT(model="whisper-1")

handle transcription server-side; server returns transcript and saves both audio+transcript to DB. Python server endpoint (FastAPI + PyMongo + GridFS) — conceptual • This endpoint accepts uploaded audio chunks (multipart) and optional transcript text, saves audio to GridFS, and stores metadata + transcript in a collection.

from fastapi import FastAPI, File, UploadFile, Form
from pymongo import MongoClient
import gridfs
import datetime
import uuid

app = FastAPI()
mongo = MongoClient("<mongodb://localhost:27017/>")
db = mongo["voice_conversations"]
fs = gridfs.GridFSBucket(db)
transcripts_col = db["transcripts"]

@app.post("/api/upload_audio_chunk")
async def upload_audio_chunk(
    session_id: str = Form(...),
    user_id: str = Form(...),
    part_index: int = Form(...),
    timestamp: str = Form(...),
    transcript: str = Form(None),
    audio: UploadFile = File(...)
):
    # read bytes (streaming is better for large files)
    audio_bytes = await audio.read()

    # store audio in GridFS
    filename = f"{session_id}_{user_id}_part{part_index}_{uuid.uuid4().hex}.webm"
    upload_stream = fs.open_upload_stream(filename, metadata={
        "session_id": session_id,
        "user_id": user_id,
        "part_index": part_index,
        "timestamp": timestamp
    })
    upload_stream.write(audio_bytes)
    upload_stream.close()
    file_id = upload_stream._file_id

    # store transcript metadata and link to audio file
    doc = {
        "session_id": session_id,
        "user_id": user_id,
        "part_index": part_index,
        "timestamp": datetime.datetime.fromisoformat(timestamp),
        "transcript": transcript,
        "audio_file_id": file_id,
        "created_at": datetime.datetime.utcnow()
    }
    transcripts_col.insert_one(doc)

    return {"ok": True, "file_id": str(file_id)}

Notes on audio format and encoding • Browser MediaRecorder often gives WebM/Opus chunks (good for size). You can store those as-is in GridFS and later decode if needed. • If you prefer WAV or MP3 for compatibility, server-side can transcode (ffmpeg) after upload. • Keep chunk sizes small (5–30s) to simplify uploads, fault tolerance, and fast transcription. Transcription options • Client-side STT: quicker to prototype but lower accuracy and limited models. • Server-side STT (recommended): send audio chunks to your agent backend (which you’re already running). The agent can run your configured STT (e.g., Whisper through OpenAI STT), produce a transcript, and then call the same MongoDB save with transcript+audio reference. This keeps transcription consistent and centralized. • Correlate transcripts and audio with the same session_id, part_index, and timestamps. How to wire this into your existing Python agent code • You already create and start AgentSession with

stt=openai.STT(model="whisper-1")

. A robust pattern: a. Client uploads audio chunks to your FastAPI endpoint. b. Your endpoint enqueues chunk processing (e.g., Celery or an asyncio task). c. A server worker uses your

openai.STT

implementation to transcribe the chunk, then saves transcript + GridFS file id to MongoDB. d. Optionally, push the transcript to the room or agent via LiveKit data messages so the agent reacts in near real-time. Example integration idea (in your job flow): • When participant joins, generate session_id and send it in token/metadata to client. • Client records and uploads chunks with that session_id. • Server stores audio + transcript; if needed, agent code (your entrypoint) can query MongoDB for transcripts to build context or continue conversation. Why agent/backend is often preferable (brief) • You already have STT configured server-side and session context — easy to pair audio+transcript. • Better security and consistent transcript quality. • Avoid browser compatibility and reliability issues. • You can stream audio straight from tracks on the agent side later for production. Next step and one quick question • Do you want a full client+server example that: ◦ Records in the browser (MediaRecorder + LiveKit hint), ◦ Posts chunks to a FastAPI endpoint which then calls your STT and stores results in GridFS + transcripts collection? • Also: Is your client plain JS or a framework (React/Next)? That lets me tailor the client code snippet. Sources: Speech-to-text (STT) models overview | LiveKit Documentation | Transcriber | LiveKit Documentation | External data and RAG | LiveKit Documentation

this is not link to the livekit , how will it work wit the agrnt . ylou didnot complete the code :

from fastapi import FastAPI, File, UploadFile, Form
from pymongo import MongoClient
import gridfs
import datetime
import uuid

app = FastAPI()
mongo = MongoClient("<mongodb://localhost:27017/>")
db = mongo["voice_conversations"]
fs = gridfs.GridFSBucket(db)
transcripts_col = db["transcripts"]

@app.post("/api/upload_audio_chunk")
async def upload_audio_chunk(
    session_id: str = Form(...),
    user_id: str = Form(...),
    part_index: int = Form(...),
    timestamp: str = Form(...),
    transcript: str = Form(None),
    audio: UploadFile = File(...)
):
    # read bytes (streaming is better for large files)
    audio_bytes = await audio.read()

    # store audio in GridFS
    filename = f"{session_id}_{user_id}_part{part_index}_{uuid.uuid4().hex}.webm"
    upload_stream = fs.open_upload_stream(filename, metadata={
        "session_id": session_id,
        "user_id": user_id,
        "part_index": part_index,
        "timestamp": timestamp
    })
    upload_stream.write(audio_bytes)
    upload_stream.close()
    file_id = upload_stream._file_id

    # store transcript metadata and link to audio file
    doc = {
        "session_id": session_id,
        "user_id": user_id,
        "part_index": part_index,
        "timestamp": datetime.datetime.fromisoformat(timestamp),
        "transcript": transcript,
        "audio_file_id": file_id,
        "created_at": datetime.datetime.utcnow()
    }
    transcripts_col.insert_one(doc)

    return {"ok": True, "file_id": str(file_id)}

Thanks — I’m responding to your note that the FastAPI endpoint looked disconnected from your LiveKit agent code. You’re right: I didn’t show the glue that links LiveKit (your agent/session) with the upload endpoint and transcription flow. Below I’ll give a complete, concrete, end-to-end plan and code sketches that show how the pieces connect: • Browser client records audio (from mic or LiveKit track) and uploads chunks to your FastAPI endpoint. • FastAPI endpoint stores audio in GridFS and enqueues transcription. • Your Python agent (the same process running AgentSession) either: ◦ Pulls transcripts from MongoDB as they arrive, or ◦ Receives transcription results pushed back from the FastAPI worker (via an internal queue or direct DB write + notification). • The agent can then use the transcript in-session (e.g., send it to WAECExamAgent) and Mongo holds audio + transcript linked by session_id + part_index. Before code: one quick question • Is your frontend plain JavaScript or a framework (React/Next/etc.)? I’ll tailor the client snippet accordingly. Also confirm whether you want client-side STT (unlikely) or server-side STT (recommended). I’ll assume server-side STT using your existing OpenAI Whisper setup. End-to-end flow (concise) 1. Client records audio chunks and posts to /api/upload_audio_chunk with metadata: session_id, user_id, part_index, timestamp. 2. FastAPI saves audio to GridFS and inserts a pending doc in a transcripts queue collection. 3. A transcription worker (could be an asyncio task in the same FastAPI app, a background worker, or part of your agent process) picks up the pending doc, runs STT (OpenAI/Whisper), writes the final transcript document to transcripts collection, and optionally notifies the agent. 4. Your agent (the code you posted) watches MongoDB (change stream) or receives a notification and then pulls the transcript and uses it in-session, and you already store usage/metadata in your DB. Concrete code pieces A) Client: capture mic with MediaRecorder and upload chunks (Plain JS; change MIME if you prefer)

// client.js (plain JS)
async function startRecording(sessionId, userId) {
  const stream = await navigator.mediaDevices.getUserMedia({ audio: true });
  const recorder = new MediaRecorder(stream, { mimeType: 'audio/webm;codecs=opus' });
  let partIndex = 0;

  recorder.ondataavailable = async (e) => {
    if (!e.data || e.data.size === 0) return;
    const form = new FormData();
    form.append('session_id', sessionId);
    form.append('user_id', userId);
    form.append('part_index', partIndex);
    form.append('timestamp', new Date().toISOString());
    form.append('audio', e.data, `chunk-${partIndex}.webm`);
    // optional: include interim transcript if you have one
    await fetch('/api/upload_audio_chunk', {
      method: 'POST',
      body: form,
      credentials: 'include'
    });
    partIndex += 1;
  };

  recorder.start(5000); // 5s chunks
  return () => recorder.stop(); // returns stop function
}

If you want to record a remote LiveKit audio track instead of the local mic, capture the MediaStream from the LiveKit track or route the track to an AudioContext and record the stream similarly (I can provide details if you need that). B) FastAPI endpoint (complete, with enqueue) This expands your endpoint so it writes a pending job doc that a worker will transcribe. It returns immediately so client uploads are fast.

# server.py
from fastapi import FastAPI, File, UploadFile, Form
from pymongo import MongoClient
import gridfs
import datetime
import uuid
import asyncio

app = FastAPI()
mongo = MongoClient("<mongodb://localhost:27017/>")
db = mongo["voice_conversations"]
fs = gridfs.GridFSBucket(db)
transcripts_col = db["transcripts"]
jobs_col = db["stt_jobs"]  # pending transcription jobs

@app.post("/api/upload_audio_chunk")
async def upload_audio_chunk(
    session_id: str = Form(...),
    user_id: str = Form(...),
    part_index: int = Form(...),
    timestamp: str = Form(...),
    audio: UploadFile = File(...),
):
    # Stream read (for larger files you might read in chunks)
    audio_bytes = await audio.read()

    filename = f"{session_id}_{user_id}_part{part_index}_{uuid.uuid4().hex}.webm"
    upload_stream = fs.open_upload_stream(filename, metadata={
        "session_id": session_id,
        "user_id": user_id,
        "part_index": int(part_index),
        "timestamp": timestamp
    })
    upload_stream.write(audio_bytes)
    upload_stream.close()
    file_id = upload_stream._file_id

    # Insert a pending STT job
    job = {
        "session_id": session_id,
        "user_id": user_id,
        "part_index": int(part_index),
        "audio_file_id": file_id,
        "status": "pending",
        "created_at": datetime.datetime.utcnow()
    }
    jobs_col.insert_one(job)

    return {"ok": True, "job_id": str(job["_id"]), "file_id": str(file_id)}

C) Transcription worker (can run alongside your agent) This example runs as an asyncio background task inside your agent process or as a separate worker. It polls pending jobs, downloads audio bytes from GridFS, calls your STT (OpenAI Whisper wrapper), stores transcript, and marks job done. Replace

run_stt_on_audio_bytes

with your existing STT call.

import time
import threading
from pymongo import ReturnDocument

def run_stt_on_audio_bytes(audio_bytes: bytes) -> str:
    # Call your existing STT wrapper, e.g., openai.STT
    # For example: session.stt.transcribe_bytes(audio_bytes)
    # This function must return the transcript text.
    # Placeholder:
    return "transcribed text here"

def stt_worker(poll_interval=1.0):
    while True:
        job = jobs_col.find_one_and_update(
            {"status": "pending"},
            {"$set": {"status": "processing", "started_at": datetime.datetime.utcnow()}},
            sort=[("created_at", 1)],
            return_document=ReturnDocument.AFTER
        )
        if not job:
            time.sleep(poll_interval)
            continue

        try:
            # Fetch audio bytes from GridFS
            bucket = fs
            stream = bucket.open_download_stream(job["audio_file_id"])
            audio_bytes = stream.read()
            stream.close()

            # Run STT (use your configured OpenAI Whisper or other)
            transcript_text = run_stt_on_audio_bytes(audio_bytes)

            # Store transcript with metadata
            transcripts_col.insert_one({
                "session_id": job["session_id"],
                "user_id": job["user_id"],
                "part_index": job["part_index"],
                "audio_file_id": job["audio_file_id"],
                "transcript": transcript_text,
                "created_at": datetime.datetime.utcnow()
            })

            # Mark job done
            jobs_col.update_one({"_id": job["_id"]}, {"$set": {"status": "done", "finished_at": datetime.datetime.utcnow()}})
        except Exception as e:
            jobs_col.update_one({"_id": job["_id"]}, {"$set": {"status": "error", "error": str(e)}})

# Option: start worker in a background thread/process when your server starts
thread = threading.Thread(target=stt_worker, daemon=True)
thread.start()

D) Integrate with your AgentSession (use transcripts in-session) You can either: • Let the agent poll the transcripts collection for new entries with the same session_id and part_index, or • Use MongoDB change streams to get real-time notifications. Below is a simple polling pattern to pull new transcripts and feed them into your WAECExamAgent. Add this into your entrypoint after session.start():

async def pull_transcripts_and_feed(agent, session_id, seen=set()):
    while True:
        cursor = transcripts_col.find({"session_id": session_id}).sort("created_at", 1)
        for doc in cursor:
            key = (doc["user_id"], doc["part_index"], doc["created_at"])
            if key in seen:
                continue
            seen.add(key)
            transcript_text = doc["transcript"]
            # Now feed to agent: the exact API depends on your agent, example:
            await agent.process_user_transcript(transcript_text, metadata={
                "user_id": doc["user_id"],
                "part_index": doc["part_index"],
                "audio_file_id": doc["audio_file_id"]
            })
        await asyncio.sleep(1.0)

# start background task
asyncio.create_task(pull_transcripts_and_feed(agent, session_id=ctx.room.name))

Alternatively use MongoDB change streams if your Mongo deployment supports it; that delivers low-latency notifications. E) How agent can save audio+transcript together You already save chat history in your job shutdown code. When a transcript is created (see C), transcripts_col includes

audio_file_id

. Your agent can query transcripts_col for the session and link audio and transcript when saving conversation summaries. Production notes and best practices • Use GridFS for files >16MB. Chunking (5–30s) will avoid huge single

i mean the vocie conversation..where id he code for that

I don't have the answer you're looking for. You could also try asking your question: • in one of the other Slack channels or • to https://deepwiki.com/livekit/livekit_composite which is trained on all LiveKit source code If you find the answer, please post it here to help others!

i mean saving the voice conversation