Overcoming Session Duration Limit

To overcome the session duration limits imposed by Google's Gemini 2 Flash API (15 minutes for audio or 2 minutes for audio and video), you need to ensure that each WebSocket connection does not exceed these time constraints. One effective way to achieve this is by establishing a new WebSocket connection for each dialogue instead of maintaining a single long-lived connection. This approach ensures that each interaction stays within the allowed session duration, thereby preventing unexpected connection terminations.

Below, I'll guide you through modifying your existing code to implement this strategy. The key changes involve refactoring the AudioGenerator class to handle individual connections per dialogue and updating the run method accordingly.

Key Modifications:

1. Establishing Connections Per Dialogue:

   • Instead of opening a single WebSocket connection for all dialogues, open a new connection for each dialogue.

2. Refactoring the run Method:

   • Modify the run method to iterate over each dialogue, establish a connection, send the text, receive the audio, and then close the connection.

3. Handling Retries Individually:

   • Implement retry logic for each dialogue to handle transient connection issues without affecting other dialogues.

4. Optimizing Progress Tracking:

   • Update the progress bar after each dialogue is processed to provide real-time feedback.

Updated Code:

Below is the refactored version of your code with the necessary modifications to handle the session duration limits effectively.

python
import tempfile
import asyncio
import base64
import json
import os
import wave
from websockets.asyncio.client import connect
import websockets
import pyaudio
from dotenv import load_dotenv
import sys
from pydub import AudioSegment
from tqdm import tqdm  # Import tqdm for the progress bar

load_dotenv()

GOOGLE_API_KEY = os.getenv('GOOGLE_API_KEY')
VOICE_A = os.getenv('VOICE_A', 'Puck')
VOICE_B = os.getenv('VOICE_B', 'Kore')

if sys.version_info < (3, 11):
    import taskgroup, exceptiongroup
    asyncio.TaskGroup = taskgroup.TaskGroup
    asyncio.ExceptionGroup = exceptiongroup.ExceptionGroup

class AudioGenerator:
    def __init__(self, progress_bar=None):
        self.progress_bar = progress_bar  # Initialize the progress bar

        # Audio configuration
        self.FORMAT = pyaudio.paInt16
        self.CHANNELS = 2
        self.SAMPLE_RATE = 24000
        self.CHUNK_SIZE = 512

        # API configuration
        self.host = 'generativelanguage.googleapis.com'
        self.model = "gemini-2.0-flash-exp"
        self.base_uri = f"wss://{self.host}/ws/google.ai.generativelanguage.v1alpha.GenerativeService.BidiGenerateContent?key={GOOGLE_API_KEY}"

        # WebSocket configuration
        self.ws_options = {
            'ping_interval': 20,
            'ping_timeout': 10,
            'close_timeout': 5
        }

    async def process_dialogue(self, dialogue, voice, output_file, max_retries=3):
        attempt = 0
        while attempt < max_retries:
            try:
                async with connect(self.base_uri, **self.ws_options) as ws:
                    # Setup message
                    setup_msg = {
                        "setup": {
                            "model": f"models/{self.model}",
                            "generation_config": {
                                "speech_config": {
                                    "voice_config": {
                                        "prebuilt_voice_config": {
                                            "voice_name": voice
                                        }
                                    }
                                }
                            }
                        }
                    }
                    await ws.send(json.dumps(setup_msg))
                    setup_response = await ws.recv()
                    # Optionally, handle setup_response if needed

                    # Send dialogue text
                    msg = {
                        "client_content": {
                            "turn_complete": True,
                            "turns": [
                                {"role": "user", "parts": [{"text": dialogue}]}
                            ]
                        }
                    }
                    await ws.send(json.dumps(msg))

                    # Receive audio data
                    complete_audio = bytearray()
                    async for raw_response in ws:
                        response = json.loads(raw_response)

                        # Process audio data
                        try:
                            parts = response["serverContent"]["modelTurn"]["parts"]
                            for part in parts:
                                if "inlineData" in part:
                                    b64data = part["inlineData"]["data"]
                                    pcm_data = base64.b64decode(b64data)
                                    complete_audio.extend(pcm_data)
                        except KeyError:
                            pass

                        # Check for completion
                        try:
                            if response["serverContent"].get("turnComplete", False):
                                self.save_wav_file(complete_audio, output_file)
                                break
                        except KeyError:
                            pass
                    return  # Successfully processed the dialogue
            except websockets.exceptions.ConnectionClosedError as e:
                attempt += 1
                if attempt < max_retries:
                    print(f"Connection closed: {e}. Retrying in 5 seconds... (Attempt {attempt}/{max_retries})")
                    await asyncio.sleep(5)
                else:
                    print(f"Max retries reached for dialogue: {dialogue}. Skipping.")
                    raise e
            except Exception as e:
                attempt += 1
                if attempt < max_retries:
                    print(f"Error: {e}. Retrying in 5 seconds... (Attempt {attempt}/{max_retries})")
                    await asyncio.sleep(5)
                else:
                    print(f"Max retries reached for dialogue: {dialogue}. Skipping.")
                    raise e

    def save_wav_file(self, audio_data, filename):
        with wave.open(filename, 'wb') as wav_file:
            wav_file.setnchannels(self.CHANNELS)
            wav_file.setsampwidth(2)
            wav_file.setframerate(self.SAMPLE_RATE)
            # Convert mono to stereo by duplicating the audio data
            stereo_data = bytearray()
            for i in range(0, len(audio_data), 2):
                sample = audio_data[i:i+2]
                stereo_data.extend(sample)  # Left channel
                stereo_data.extend(sample)  # Right channel
            wav_file.writeframes(stereo_data)
        # Update the progress bar instead of printing
        if self.progress_bar:
            self.progress_bar.update(1)
        # Optionally, you can remove the following line if you don't want any output
        # print(f"Audio saved to {filename}")

    async def run(self, dialogues, voices, output_files):
        tasks = []
        for dialogue, voice, output_file in zip(dialogues, voices, output_files):
            tasks.append(self.process_dialogue(dialogue, voice, output_file))
        await asyncio.gather(*tasks)

def parse_conversation(file_path):
    with open(file_path, 'r', encoding='utf-8') as file:
        content = file.read()
    
    lines = content.strip().split('\n')
    speaker_a_lines = []
    speaker_b_lines = []
    
    for line in lines:
        if line.strip():
            if line.startswith("Speaker A:"):
                speaker_a_lines.append(line.replace("Speaker A:", "").strip())
            elif line.startswith("Speaker B:"):
                speaker_b_lines.append(line.replace("Speaker B:", "").strip())
    
    return speaker_a_lines, speaker_b_lines

def combine_audio_files(file_list, output_file):
    combined = AudioSegment.empty()
    for file in file_list:
        audio = AudioSegment.from_wav(file)
        # Ensure stereo
        if audio.channels == 1:
            audio = audio.set_channels(2)
        combined += audio
    combined.export(output_file, format="wav")

def read_file_content(file_path):
    with open(file_path, 'r', encoding='utf-8') as file:
        return file.read()

async def setup_environment():
    if not os.getenv('GOOGLE_API_KEY'):
        raise EnvironmentError("GOOGLE_API_KEY not found in environment variables")
    script_dir = os.path.dirname(os.path.abspath(__file__))
    return script_dir

def read_and_parse_inputs():
    system_instructions = read_file_content('system_instructions_audio.txt')
    full_script = read_file_content('podcast_script.txt')
    speaker_a_lines, speaker_b_lines = parse_conversation('podcast_script.txt')
    return system_instructions, full_script, speaker_a_lines, speaker_b_lines

def prepare_speaker_dialogues(system_instructions, full_script, speaker_lines, voice, temp_dir):
    dialogues = [system_instructions + "\n\n" + full_script]
    voices = [voice]
    output_files = [os.path.join(temp_dir, f"speaker_{voice}_initial.wav")]

    for i, line in enumerate(speaker_lines):
        dialogues.append(line)
        voices.append(voice)
        output_files.append(os.path.join(temp_dir, f"speaker_{voice}_{i}.wav"))

    return dialogues, voices, output_files

async def process_speaker(generator, dialogues, output_files, voices):
    await generator.run(dialogues, voices, output_files)

def interleave_output_files(speaker_a_files, speaker_b_files):
    all_output_files = []
    min_length = min(len(speaker_a_files), len(speaker_b_files))

    for i in range(min_length):
        all_output_files.extend([speaker_a_files[i], speaker_b_files[i]])

    all_output_files.extend(speaker_a_files[min_length:])
    all_output_files.extend(speaker_b_files[min_length:])

    return all_output_files

async def main():
    script_dir = await setup_environment()

    with tempfile.TemporaryDirectory(dir=script_dir) as temp_dir:
        system_instructions, full_script, speaker_a_lines, speaker_b_lines = read_and_parse_inputs()

        dialogues_a, voices_a, output_files_a = prepare_speaker_dialogues(
            system_instructions, full_script, speaker_a_lines, VOICE_A, temp_dir)
        dialogues_b, voices_b, output_files_b = prepare_speaker_dialogues(
            system_instructions, full_script, speaker_b_lines, VOICE_B, temp_dir)

        # Calculate total number of audio files to generate
        total_audio_files = len(output_files_a) + len(output_files_b)

        # Initialize the tqdm progress bar
        with tqdm(total=total_audio_files, desc="Generating audio files", unit="file") as progress_bar:
            # Initialize the AudioGenerator with the progress bar
            generator = AudioGenerator(progress_bar=progress_bar)

            # Process both speakers concurrently
            await asyncio.gather(
                process_speaker(generator, dialogues_a, output_files_a, voices_a),
                process_speaker(generator, dialogues_b, output_files_b, voices_b)
            )

            # No need to manually close the progress bar as it's handled by the context manager

        all_output_files = interleave_output_files(output_files_a[1:], output_files_b[1:])
        final_output = "final_podcast.wav"
        combine_audio_files(all_output_files, final_output)
        print(f"\nFinal podcast audio created: {final_output}")

    print("Temporary files cleaned up")

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

Detailed Explanation of Changes:

1. Processing Dialogues Individually:

   • process_dialogue Method: A new method process_dialogue is introduced within the AudioGenerator class. This method handles the entire lifecycle of a single dialogue: establishing a connection, sending the setup and dialogue messages, receiving the audio, and saving the audio file.
   • Retry Logic: The method includes retry logic to handle transient errors. If a connection is closed unexpectedly, it retries up to max_retries times before skipping the problematic dialogue.

2. Refactored run Method:

   • The run method now iterates over each dialogue, voice, and output file, creating a list of tasks that are executed concurrently using asyncio.gather. This ensures that each dialogue is processed in its own connection without exceeding session limits.

3. Progress Bar Integration:

   • The progress bar is updated within the save_wav_file method after each audio file is successfully saved. This provides real-time feedback on the progress of audio generation.

4. Handling Multiple Speakers:

   • The main function processes both Speaker A and Speaker B concurrently by creating separate lists of dialogues, voices, and output files for each speaker. This parallel processing optimizes the overall execution time while maintaining session limits.

5. Combining Audio Files:

   • After all dialogues are processed, the audio files are interleaved and combined into a final podcast audio file using the combine_audio_files function.

Benefits of This Approach:

• Compliance with Session Limits: By establishing a new WebSocket connection for each dialogue, you ensure that no single connection exceeds the 15-minute (or 2-minute) session duration limit.

• Robustness: The retry mechanism ensures that transient network issues or temporary API outages do not halt the entire audio generation process.

• Scalability: Processing dialogues concurrently (while still respecting session limits) can significantly speed up the overall execution time, especially for large scripts.

• Maintainability: The refactored code is modular, making it easier to understand, maintain, and extend in the future.

Additional Recommendations:

1. Rate Limiting: Depending on the API's rate limits, you might need to implement rate limiting to avoid exceeding allowed request rates. You can use tools like asyncio.Semaphore to control the number of concurrent connections.

2. Logging: Implementing a more sophisticated logging mechanism can help in debugging and monitoring the process, especially when dealing with a large number of dialogues.

3. Error Handling Enhancements: Consider handling specific exceptions more gracefully and providing more informative error messages to aid in troubleshooting.

4. Configuration Management: Externalize parameters like max_retries, retry_delay, and others to configuration files or environment variables for easier adjustments without modifying the code.

By implementing these changes, your application will be better equipped to handle the session duration limits imposed by Google's Gemini 2 Flash API, ensuring a smooth and uninterrupted audio generation process.

I apologize for the inconvenience you're experiencing. The error message:

Connection closed: received 1011 (internal error) Request trace id: 28b49dfaa440f090, Quota exceeded for quota metric 'Bidi generate content active sessions' and limit 'Bidi. Retrying in 5 seconds... (Attempt 1/3)

indicates that your application is exceeding the quota for active bidirectional (Bidi) generate content sessions. This typically happens when too many WebSocket connections are opened simultaneously, surpassing the allowed limit set by the Google API.

To resolve this issue, you need to limit the number of concurrent WebSocket connections. The most straightforward way to achieve this is by processing dialogues sequentially rather than concurrently. This ensures that only one active session is maintained at any given time, adhering to the quota restrictions.

Below, I'll provide a revised version of your code that processes each dialogue one by one, ensuring compliance with the session limits. Additionally, I'll include some best practices and enhancements to improve the overall robustness of your application.

Revised Code: Sequential Processing of Dialogues

python
import tempfile
import asyncio
import base64
import json
import os
import wave
from websockets.asyncio.client import connect
import websockets
import pyaudio
from dotenv import load_dotenv
import sys
from pydub import AudioSegment
from tqdm import tqdm  # Import tqdm for the progress bar

load_dotenv()

GOOGLE_API_KEY = os.getenv('GOOGLE_API_KEY')
VOICE_A = os.getenv('VOICE_A', 'Puck')
VOICE_B = os.getenv('VOICE_B', 'Kore')

if sys.version_info < (3, 11):
    import taskgroup, exceptiongroup
    asyncio.TaskGroup = taskgroup.TaskGroup
    asyncio.ExceptionGroup = exceptiongroup.ExceptionGroup

class AudioGenerator:
    def __init__(self, progress_bar=None):
        self.progress_bar = progress_bar  # Initialize the progress bar

        # Audio configuration
        self.FORMAT = pyaudio.paInt16
        self.CHANNELS = 2
        self.SAMPLE_RATE = 24000
        self.CHUNK_SIZE = 512

        # API configuration
        self.host = 'generativelanguage.googleapis.com'
        self.model = "gemini-2.0-flash-exp"
        self.base_uri = f"wss://{self.host}/ws/google.ai.generativelanguage.v1alpha.GenerativeService.BidiGenerateContent?key={GOOGLE_API_KEY}"

        # WebSocket configuration
        self.ws_options = {
            'ping_interval': 20,
            'ping_timeout': 10,
            'close_timeout': 5
        }

    async def process_dialogue(self, dialogue, voice, output_file, max_retries=3):
        attempt = 0
        while attempt < max_retries:
            try:
                async with connect(self.base_uri, **self.ws_options) as ws:
                    # Setup message
                    setup_msg = {
                        "setup": {
                            "model": f"models/{self.model}",
                            "generation_config": {
                                "speech_config": {
                                    "voice_config": {
                                        "prebuilt_voice_config": {
                                            "voice_name": voice
                                        }
                                    }
                                }
                            }
                        }
                    }
                    await ws.send(json.dumps(setup_msg))
                    setup_response = await ws.recv()
                    # Optionally, handle setup_response if needed

                    # Send dialogue text
                    msg = {
                        "client_content": {
                            "turn_complete": True,
                            "turns": [
                                {"role": "user", "parts": [{"text": dialogue}]}
                            ]
                        }
                    }
                    await ws.send(json.dumps(msg))

                    # Receive audio data
                    complete_audio = bytearray()
                    async for raw_response in ws:
                        response = json.loads(raw_response)

                        # Process audio data
                        try:
                            parts = response["serverContent"]["modelTurn"]["parts"]
                            for part in parts:
                                if "inlineData" in part:
                                    b64data = part["inlineData"]["data"]
                                    pcm_data = base64.b64decode(b64data)
                                    complete_audio.extend(pcm_data)
                        except KeyError:
                            pass

                        # Check for completion
                        try:
                            if response["serverContent"].get("turnComplete", False):
                                self.save_wav_file(complete_audio, output_file)
                                break
                        except KeyError:
                            pass
                    return  # Successfully processed the dialogue
            except websockets.exceptions.ConnectionClosedError as e:
                attempt += 1
                if attempt < max_retries:
                    print(f"Connection closed: {e}. Retrying in 5 seconds... (Attempt {attempt}/{max_retries})")
                    await asyncio.sleep(5)
                else:
                    print(f"Max retries reached for dialogue: {dialogue}. Skipping.")
                    raise e
            except Exception as e:
                attempt += 1
                if attempt < max_retries:
                    print(f"Error: {e}. Retrying in 5 seconds... (Attempt {attempt}/{max_retries})")
                    await asyncio.sleep(5)
                else:
                    print(f"Max retries reached for dialogue: {dialogue}. Skipping.")
                    raise e

    def save_wav_file(self, audio_data, filename):
        with wave.open(filename, 'wb') as wav_file:
            wav_file.setnchannels(self.CHANNELS)
            wav_file.setsampwidth(2)
            wav_file.setframerate(self.SAMPLE_RATE)
            # Convert mono to stereo by duplicating the audio data
            stereo_data = bytearray()
            for i in range(0, len(audio_data), 2):
                sample = audio_data[i:i+2]
                stereo_data.extend(sample)  # Left channel
                stereo_data.extend(sample)  # Right channel
            wav_file.writeframes(stereo_data)
        # Update the progress bar instead of printing
        if self.progress_bar:
            self.progress_bar.update(1)
        # Optionally, you can remove the following line if you don't want any output
        # print(f"Audio saved to {filename}")

    async def run(self, dialogues, voices, output_files):
        for dialogue, voice, output_file in zip(dialogues, voices, output_files):
            await self.process_dialogue(dialogue, voice, output_file)

def parse_conversation(file_path):
    with open(file_path, 'r', encoding='utf-8') as file:
        content = file.read()
    
    lines = content.strip().split('\n')
    speaker_a_lines = []
    speaker_b_lines = []
    
    for line in lines:
        if line.strip():
            if line.startswith("Speaker A:"):
                speaker_a_lines.append(line.replace("Speaker A:", "").strip())
            elif line.startswith("Speaker B:"):
                speaker_b_lines.append(line.replace("Speaker B:", "").strip())
    
    return speaker_a_lines, speaker_b_lines

def combine_audio_files(file_list, output_file):
    combined = AudioSegment.empty()
    for file in file_list:
        audio = AudioSegment.from_wav(file)
        # Ensure stereo
        if audio.channels == 1:
            audio = audio.set_channels(2)
        combined += audio
    combined.export(output_file, format="wav")

def read_file_content(file_path):
    with open(file_path, 'r', encoding='utf-8') as file:
        return file.read()

async def setup_environment():
    if not os.getenv('GOOGLE_API_KEY'):
        raise EnvironmentError("GOOGLE_API_KEY not found in environment variables")
    script_dir = os.path.dirname(os.path.abspath(__file__))
    return script_dir

def read_and_parse_inputs():
    system_instructions = read_file_content('system_instructions_audio.txt')
    full_script = read_file_content('podcast_script.txt')
    speaker_a_lines, speaker_b_lines = parse_conversation('podcast_script.txt')
    return system_instructions, full_script, speaker_a_lines, speaker_b_lines

def prepare_speaker_dialogues(system_instructions, full_script, speaker_lines, voice, temp_dir):
    dialogues = [system_instructions + "\n\n" + full_script]
    voices = [voice]
    output_files = [os.path.join(temp_dir, f"speaker_{voice}_initial.wav")]

    for i, line in enumerate(speaker_lines):
        dialogues.append(line)
        voices.append(voice)
        output_files.append(os.path.join(temp_dir, f"speaker_{voice}_{i}.wav"))

    return dialogues, voices, output_files

async def process_speaker(generator, dialogues, output_files, voices):
    await generator.run(dialogues, voices, output_files)

def interleave_output_files(speaker_a_files, speaker_b_files):
    all_output_files = []
    min_length = min(len(speaker_a_files), len(speaker_b_files))

    for i in range(min_length):
        all_output_files.extend([speaker_a_files[i], speaker_b_files[i]])

    all_output_files.extend(speaker_a_files[min_length:])
    all_output_files.extend(speaker_b_files[min_length:])

    return all_output_files

async def main():
    script_dir = await setup_environment()

    with tempfile.TemporaryDirectory(dir=script_dir) as temp_dir:
        system_instructions, full_script, speaker_a_lines, speaker_b_lines = read_and_parse_inputs()

        dialogues_a, voices_a, output_files_a = prepare_speaker_dialogues(
            system_instructions, full_script, speaker_a_lines, VOICE_A, temp_dir)
        dialogues_b, voices_b, output_files_b = prepare_speaker_dialogues(
            system_instructions, full_script, speaker_b_lines, VOICE_B, temp_dir)

        # Calculate total number of audio files to generate
        total_audio_files = len(output_files_a) + len(output_files_b)

        # Initialize the tqdm progress bar
        with tqdm(total=total_audio_files, desc="Generating audio files", unit="file") as progress_bar:
            # Initialize the AudioGenerator with the progress bar
            generator = AudioGenerator(progress_bar=progress_bar)

            # Process Speaker A
            await process_speaker(generator, dialogues_a, output_files_a, voices_a)
            # Process Speaker B
            await process_speaker(generator, dialogues_b, output_files_b, voices_b)

            # No need to manually close the progress bar as it's handled by the context manager

        all_output_files = interleave_output_files(output_files_a[1:], output_files_b[1:])
        final_output = "final_podcast.wav"
        combine_audio_files(all_output_files, final_output)
        print(f"\nFinal podcast audio created: {final_output}")

    print("Temporary files cleaned up")

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

Key Changes and Explanations

1. Sequential Processing of Dialogues:

   • Previous Approach: The original modification processed both speakers concurrently using asyncio.gather, leading to multiple active WebSocket connections simultaneously. This exceeded the quota for active sessions.

   • Revised Approach: The updated code processes each dialogue sequentially. This is achieved by iterating over each dialogue in the run method without using asyncio.gather. This ensures that only one WebSocket connection is active at any given time, adhering to the quota limits.

   python
   async def run(self, dialogues, voices, output_files):
       for dialogue, voice, output_file in zip(dialogues, voices, output_files):
           await self.process_dialogue(dialogue, voice, output_file)

2. Processing Speakers Sequentially:

   • Previous Approach: Both Speaker A and Speaker B were processed concurrently, which likely resulted in multiple active sessions and quota exhaustion.

   • Revised Approach: Speakers are now processed one after the other. First, all dialogues for Speaker A are processed, followed by Speaker B. This further ensures that only one active session exists at any point.

   python
   # Process Speaker A
   await process_speaker(generator, dialogues_a, output_files_a, voices_a)
   # Process Speaker B
   await process_speaker(generator, dialogues_b, output_files_b, voices_b)

3. Removed Concurrent Task Execution:

   • The use of asyncio.gather for processing multiple tasks concurrently has been removed to prevent exceeding the quota.

4. Enhanced Error Handling:

   • The error handling within process_dialogue has been retained to retry connections up to max_retries times in case of transient errors. If retries are exhausted, the dialogue is skipped, and the error is raised.

5. Progress Bar Integration:

   • The progress bar (tqdm) is updated after each audio file is successfully saved, providing real-time feedback on the progress of audio generation.

6. Resource Cleanup:

   • Using with tempfile.TemporaryDirectory ensures that temporary files are cleaned up automatically after processing.

Additional Recommendations

1. Monitor and Respect Quotas:

   • Understand Quotas: Review your Google Cloud Console to understand the exact quotas for your API usage. This includes the number of active sessions, requests per minute, etc.

   • Adjust Accordingly: If your application requirements exceed the current quotas, consider requesting an increase from Google. However, ensure that your application's design aligns with the quota limits to prevent interruptions.

2. Implement Rate Limiting:

   • If you anticipate spikes in usage or if processing dialogues sequentially is too slow for your needs, implement rate limiting using asyncio.Semaphore to control the number of concurrent sessions without exceeding the quota.

   python
   from asyncio import Semaphore
   
   class AudioGenerator:
       def __init__(self, progress_bar=None, max_concurrent_sessions=1):
           self.progress_bar = progress_bar
           self.semaphore = Semaphore(max_concurrent_sessions)
           # ... rest of the initialization
   
       async def process_dialogue(self, dialogue, voice, output_file, max_retries=3):
           async with self.semaphore:
               # existing process_dialogue code

   • Usage: Set max_concurrent_sessions based on your quota limits.

3. Implement Exponential Backoff:

   • Instead of retrying after a fixed delay, use exponential backoff to handle retries more gracefully, especially in high-failure scenarios.

4. Logging:

   • Implement a more robust logging mechanism to capture detailed information about successes, failures, retries, and other important events. This will aid in monitoring and debugging.

5. Optimize Dialogue Batching:

   • If possible, batch multiple dialogues within a single session to minimize the number of active sessions. However, ensure that the total duration of the session does not exceed the API's session duration limits.

6. Graceful Shutdown:

   • Ensure that your application can gracefully handle shutdown signals, closing any active WebSocket connections properly to avoid resource leaks.

Summary

By modifying your application to process dialogues sequentially, you ensure that only one active WebSocket connection exists at any given time. This approach aligns with the quota restrictions imposed by the Google Gemini 2 Flash API, preventing quota exceedance errors. Additionally, adhering to best practices like robust error handling, rate limiting, and effective resource management will enhance the reliability and maintainability of your application.

If you continue to face quota-related issues even after these adjustments, consider reaching out to Google Cloud support to discuss your usage patterns and explore potential quota increases.