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best voicepipeline parameters for rag system for m...
# ask-ai
m
best voicepipeline parameters for rag system for min latency @millions-hair-24956
g
It seems likely that optimizing a VoicePipelineAgent for minimal latency in a RAG (Retrieval-Augmented Generation) system involves adjusting several parameters. Key parameters include 
min_endpointing_delay
, 
interrupt_speech_duration
, and 
interrupt_min_words
. These parameters help control the timing of speech detection and response generation, which can impact latency. According to LiveKit documentation, setting a lower 
min_endpointing_delay
can reduce the time the system waits to determine the end of a user's speech, potentially decreasing latency. Additionally, enabling 
preemptive_synthesis
might help by starting response generation before the user finishes speaking, though this can be risky if not managed carefully. Would you like more detailed guidance on configuring these parameters for your specific use case? Sources: - LiveKit VoicePipelineAgent Parameters 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
m
yes please @millions-hair-24956
g
To optimize the VoicePipelineAgent for minimal latency in a RAG system, you can adjust several key parameters: 1. 
min_endpointing_delay
: This parameter defines the minimum silence duration required to detect the end of a user's turn. Reducing this value can help the system respond more quickly by assuming the user has finished speaking sooner. However, setting it too low might cause the system to interrupt the user prematurely. According to LiveKit documentation, a typical starting point is 0.5 seconds, but you can experiment with lower values like 0.2 seconds. 2. 
interrupt_speech_duration
: This parameter specifies the minimum duration of speech required to consider an interruption. Lowering this value can make the system more responsive to user interruptions, but it might also increase the risk of false positives. A common setting is 0.5 seconds, but you can try reducing it to 0.3 seconds for faster response times. 3. 
interrupt_min_words
: This parameter sets the minimum number of words needed to trigger an interruption. Setting this to 0 allows any speech to potentially interrupt the agent, which can be useful for quick interactions but may lead to frequent interruptions. Adjusting this to 1 or 2 words can provide a balance between responsiveness and stability. 4. 
preemptive_synthesis
: Enabling this feature allows the system to start generating a response before the user has finished speaking. This can significantly reduce latency but requires careful handling to avoid cutting off the user. As per LiveKit documentation, this feature is not enabled by default, so you will need to explicitly set it to 
True
. 5. Network and Deployment Considerations: Ensure that your agent is deployed close to the model servers to minimize network latency. Running the agent in the same region as the model servers can significantly reduce round-trip times for API calls. Here's an example configuration:
Copy code
agent = VoicePipelineAgent(
    vad=silero.VAD.load(),
    stt=deepgram.STT(model="nova-2"),
    llm=openai.LLM(model="gpt-3.5-turbo"),
    tts=cartesia.TTS(),
    chat_ctx=initial_ctx,
    allow_interruptions=True,
    interrupt_speech_duration=0.3,
    interrupt_min_words=1,
    min_endpointing_delay=0.2,
    preemptive_synthesis=True,
    before_llm_cb=default_before_llm_cb,
    before_tts_cb=default_before_tts_cb
)
By carefully tuning these parameters, you can achieve a more responsive and efficient RAG system with minimal latency. Sources: - LiveKit VoicePipelineAgent Parameters 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
m
but llm=openai.LLM(model="gpt-3.5-turbo"), @millions-hair-24956 why u dıdınt select gpt-4?
g
It seems likely that the choice of 
gpt-3.5-turbo
over 
gpt-4
in the example was made to balance performance and latency. According to LiveKit documentation, 
gpt-3.5-turbo
is generally faster and more cost-effective than 
gpt-4
, which can be beneficial in applications where low latency is critical. However, if your application requires the advanced capabilities of 
gpt-4
, you can certainly use it by specifying 
gpt-4
or any of its variants in the 
model
parameter. Would you like more information on the trade-offs between these models or how to configure them in LiveKit? Sources: - LiveKit documentation 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
m
my voicepipeline agent , for mın latency ı amde your changes , and deploy server to us , but my agent s fist response fter welcomıng message latency is so long , how to reduxe this ? @millions-hair-24956
g
To address the latency issue for the first response after the welcoming message, consider the following strategies: 1. Pre-warm the LLM: Pre-warming the LLM can help reduce the initial latency. This involves sending a dummy request to the LLM at startup to ensure it's ready to process real requests quickly. Unfortunately, specific pre-warming methods are not detailed in the provided data sources, but you can simulate this by making an initial call to the LLM with a simple prompt. 2. Optimize Network Latency: Ensure that your server is geographically close to both the LiveKit infrastructure and the LLM service (e.g., OpenAI). Deploying in regions like US-East or US-Central can help minimize latency, as suggested in LiveKit Slack discussions. 3. Use Streaming Responses: If supported by your LLM, enable streaming responses to start receiving output as soon as it's generated, rather than waiting for the entire response. This can significantly reduce perceived latency. 4. Adjust Turn Detection: Fine-tune the Voice Activity Detection (VAD) parameters to ensure the system quickly detects when the user has finished speaking. This can be done by adjusting the 
threshold
, 
prefix_padding_ms
, and 
silence_duration_ms
parameters as per LiveKit documentation. 5. Caching: Implement caching for frequent responses or initial prompts to reduce the need for repeated LLM calls. Would you like more detailed guidance on any of these strategies? Sources: - LiveKit documentation on turn detection - LiveKit Slack discussions 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
m
yes more details please , @millions-hair-24956 especially prewarm llm
g
To pre-warm the LLM and reduce the initial response latency, you can follow these steps: 1. Initial Dummy Request: When your application starts, send a simple, non-critical request to the LLM. This request can be a basic prompt that doesn't require a meaningful response. The goal is to ensure that the LLM is ready to handle subsequent requests more quickly. This step helps to "wake up" the LLM and reduce the cold start time. 2. Use Pre-warm Functionality: If your LLM or infrastructure supports a pre-warm function, utilize it to prepare the model for incoming requests. While specific pre-warm functions are not detailed in the provided data sources, you can simulate this by making an initial call to the LLM with a simple prompt. 3. Optimize Deployment: Ensure that your agent is deployed in a region close to the LLM servers. This minimizes network latency and can significantly improve response times. As mentioned in LiveKit Slack discussions, deploying in US-East or US-Central regions can be beneficial. 4. Streaming Responses: If the LLM supports streaming, enable this feature to start receiving parts of the response as soon as they are generated. This can reduce the perceived latency for the user. 5. Caching: Implement caching for common or initial responses. This can reduce the need for repeated LLM calls and improve response times for frequently asked questions or standard greetings. By implementing these strategies, you can reduce the initial latency and improve the overall responsiveness of your VoicePipelineAgent. Sources: - https://docs.livekit.io/python/livekit/agents/pipeline/pipeline_agent.html#args - https://docs.livekit.io/python/livekit/agents/pipeline/index.html#args 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
m
gpt 3.5 turbo is supporting streaming ? @millions-hair-24956
g
It seems likely that GPT-3.5-turbo does support streaming, allowing you to receive parts of the response as they are generated, which can help reduce perceived latency. This feature is particularly useful in applications where quick response times are critical. However, specific implementation details for enabling streaming with GPT-3.5-turbo are not explicitly mentioned in the provided data sources. Would you like more information on how to implement streaming with GPT-3.5-turbo in your application? Sources: - https://docs.livekit.io/agents/openai - https://docs.livekit.io/agents/openai/overview/ 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
m
can you give me piece of code for voice pipeline agent gpt3.5 steamıng mode on ? @millions-hair-24956
g
It seems likely that to enable streaming mode with GPT-3.5-turbo in a VoicePipelineAgent, you would need to configure the LLM to support streaming responses. Here's a basic example of how you might set this up using LiveKit's Python SDK:
Copy code
import asyncio
from livekit import agents, rtc
from livekit.plugins import deepgram, openai, silero

async def entrypoint(ctx: agents.JobContext):
    await ctx.connect()
    print(f"Connected to room: {ctx.room.name}")

    # Initialize the chat context
    chat_context = agents.llm.ChatContext(
        messages=[
            agents.llm.ChatMessage(
                role="system",
                content="You are a helpful assistant."
            )
        ]
    )

    # Configure the LLM with streaming enabled
    gpt_llm = openai.LLM(model="gpt-3.5-turbo", streaming=True)

    # Initialize the VoicePipelineAgent
    agent = agents.VoicePipelineAgent(
        vad=silero.VAD.load(),
        stt=deepgram.STT(model="nova-2"),
        llm=gpt_llm,
        tts=openai.TTS(),
        chat_ctx=chat_context,
        allow_interruptions=True,
        interrupt_speech_duration=0.5,
        interrupt_min_words=0,
        min_endpointing_delay=0.5,
        preemptive_synthesis=True
    )

    # Start the agent
    agent.start(ctx.room)

    # Keep the connection alive
    while ctx.room.connection_state == rtc.ConnectionState.CONN_CONNECTED:
        await asyncio.sleep(1)

if __name__ == "__main__":
    agents.cli.run_app(agents.WorkerOptions(entrypoint_fnc=entrypoint))
In this example, the 
openai.LLM
is configured with 
streaming=True
to enable streaming mode. This setup allows the agent to start processing and responding to user input as soon as parts of the response are available, reducing perceived latency. Would you like more details on any specific part of this setup? Sources: - https://livekit-users.slack.com/archives/C07FY8WHGPM/p1733148578.269969 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
m
openAI's Assistant API as the LLM is better for gpt 3.5 tubo for less latency , what is difference with them ? @millions-hair-24956
g
It seems likely that the difference between using OpenAI's Assistant API and directly using GPT-3.5-turbo as the LLM in terms of latency and performance can be attributed to several factors: 1. API Optimization: The Assistant API may be optimized for specific use cases, such as conversational AI, which can result in lower latency compared to using the raw GPT-3.5-turbo model. This optimization might include better handling of context and more efficient processing of requests. 2. Feature Set: The Assistant API might offer additional features, such as built-in support for streaming responses, function calling, or other enhancements that are specifically designed to improve the responsiveness and interactivity of conversational agents. 3. Infrastructure: The infrastructure supporting the Assistant API could be optimized for lower latency, possibly through better load balancing, caching, or geographic distribution of servers, which can reduce the time it takes to process and return responses. Would you like more detailed information on how to implement or choose between these options for your specific use case? Sources: - https://livekit-users.slack.com/archives/C07FY8WHGPM/p1729014768.848989 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
m
can i sue assitant api instead of gpt , for better conservsatıon and low latency for voicepipeline agent ? @millions-hair-24956
g
It seems likely that you can use OpenAI's Assistant API instead of directly using GPT models like GPT-3.5-turbo for potentially better conversation handling and lower latency in a VoicePipelineAgent. The Assistant API is designed to optimize conversational interactions, which might include features like improved context management and faster response times due to infrastructure optimizations. To integrate the Assistant API with a VoicePipelineAgent, you would configure the LLM component of the agent to use the Assistant API. This setup can help enhance the conversational capabilities of your agent while potentially reducing latency. Would you like more detailed guidance on how to configure the VoicePipelineAgent with the Assistant API? Sources: - https://docs.livekit.io/agents/openai/multimodal-agent/ - https://docs.livekit.io/agents/openai/multimodalagent/ 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
m
what u thınk abput asistant api with rag for phone calls ? is it good for low latency , agent makıng some works like getting users phone , changıng hıs adress ? is it good for this ? we need best solutıon for conversatıonal ai @millions-hair-24956
g
Using the Assistant API with RAG (Retrieval-Augmented Generation) for phone calls can be a good solution for low-latency, conversational AI applications. The Assistant API is designed to handle conversational contexts efficiently, which can be beneficial for tasks like retrieving user information, updating addresses, and other interactive tasks. The integration of RAG allows the system to access external knowledge bases, providing more accurate and contextually relevant responses. For phone call scenarios, the Assistant API can offer several advantages: 1. Low Latency: The API is optimized for conversational interactions, which can help reduce latency, making it suitable for real-time phone call applications. 2. Context Management: The Assistant API can maintain context across interactions, which is crucial for tasks like retrieving and updating user information during a call. 3. Function Calling: The API supports function calling, allowing the agent to perform specific actions like retrieving user data or updating records, which is essential for tasks like changing a user's address. 4. Scalability: The API is designed to handle multiple concurrent interactions, making it suitable for scalable applications like call centers. Would you like more detailed guidance on implementing the Assistant API with RAG for your specific use case? Sources: - LiveKit MultimodalAgent Documentation - LiveKit MultimodalAgent Class Usage 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
m
how can i use it with voicepileine agent , give me code snippet : @millions-hair-24956
g
To use the Assistant API with a VoicePipelineAgent, you can configure the agent to utilize the Assistant API for handling conversational tasks. Here's a code snippet to illustrate how you might set this up:
Copy code
import asyncio
from livekit import agents, rtc
from livekit.plugins import deepgram, openai, silero

async def entrypoint(ctx: agents.JobContext):
    await ctx.connect()
    print(f"Connected to room: {ctx.room.name}")

    # Initialize the chat context
    chat_context = agents.llm.ChatContext(
        messages=[
            agents.llm.ChatMessage(
                role="system",
                content="You are a helpful assistant."
            )
        ]
    )

    # Configure the Assistant API as the LLM
    assistant_llm = openai.AssistantLLM(
        assistant_opts=openai.AssistantOptions(
            load_options=openai.AssistantLoadOptions(
                assistant_id="your_assistant_id",
                thread_id="your_thread_id"
            )
        )
    )

    # Initialize the VoicePipelineAgent
    agent = agents.VoicePipelineAgent(
        vad=silero.VAD.load(),
        stt=deepgram.STT(model="nova-2"),
        llm=assistant_llm,
        tts=openai.TTS(),
        chat_ctx=chat_context,
        allow_interruptions=True,
        interrupt_speech_duration=0.5,
        interrupt_min_words=0,
        min_endpointing_delay=0.5,
        preemptive_synthesis=True
    )

    # Start the agent
    agent.start(ctx.room)

    # Keep the connection alive
    while ctx.room.connection_state == rtc.ConnectionState.CONN_CONNECTED:
        await asyncio.sleep(1)

if __name__ == "__main__":
    agents.cli.run_app(agents.WorkerOptions(entrypoint_fnc=entrypoint))
In this example, the 
openai.AssistantLLM
is configured with the necessary options to use the Assistant API. You need to replace 
"your_assistant_id"
and 
"your_thread_id"
with your actual Assistant API credentials. This setup allows the VoicePipelineAgent to leverage the Assistant API for enhanced conversational capabilities. Would you like more details on any specific part of this setup? Sources: - https://livekit-users.slack.com/archives/C07FY8WHGPM/p1730293289.619029 - https://livekit-users.slack.com/archives/C07FY8WHGPM/p1733148578.269969 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
m
i want to use ıt with rag annoy , gıve me code snipper @millions-hair-24956
g
To integrate the Assistant API with a VoicePipelineAgent using RAG (Retrieval-Augmented Generation) with Annoy, you can follow these steps. This setup involves configuring the agent to use the Assistant API for conversational tasks and integrating RAG for enhanced information retrieval. Here's a code snippet to illustrate this:
Copy code
import asyncio
from livekit import agents, rtc
from livekit.plugins import deepgram, openai, silero, rag

async def entrypoint(ctx: agents.JobContext):
    await ctx.connect()
    print(f"Connected to room: {ctx.room.name}")

    # Initialize the chat context
    chat_context = agents.llm.ChatContext(
        messages=[
            agents.llm.ChatMessage(
                role="system",
                content="You are a helpful assistant."
            )
        ]
    )

    # Configure the Assistant API as the LLM
    assistant_llm = openai.AssistantLLM(
        assistant_opts=openai.AssistantOptions(
            load_options=openai.AssistantLoadOptions(
                assistant_id="your_assistant_id",
                thread_id="your_thread_id"
            )
        )
    )

    # Initialize the RAG plugin with Annoy
    rag_plugin = rag.RAG(
        index_path="path_to_your_annoy_index",
        embedding_model=openai.EmbeddingModel()
    )

    # Initialize the VoicePipelineAgent
    agent = agents.VoicePipelineAgent(
        vad=silero.VAD.load(),
        stt=deepgram.STT(model="nova-2"),
        llm=assistant_llm,
        tts=openai.TTS(),
        chat_ctx=chat_context,
        allow_interruptions=True,
        interrupt_speech_duration=0.5,
        interrupt_min_words=0,
        min_endpointing_delay=0.5,
        preemptive_synthesis=True,
        rag=rag_plugin  # Integrate RAG with the agent
    )

    # Start the agent
    agent.start(ctx.room)

    # Keep the connection alive
    while ctx.room.connection_state == rtc.ConnectionState.CONN_CONNECTED:
        await asyncio.sleep(1)

if __name__ == "__main__":
    agents.cli.run_app(agents.WorkerOptions(entrypoint_fnc=entrypoint))
In this example, the 
rag.RAG
plugin is configured to use an Annoy index for retrieval. You need to replace 
"your_assistant_id"
, 
"your_thread_id"
, and 
"path_to_your_annoy_index"
with your actual Assistant API credentials and the path to your Annoy index file. This setup allows the VoicePipelineAgent to leverage both the Assistant API and RAG for enhanced conversational capabilities and information retrieval. Would you like more details on any specific part of this setup? Sources: - https://livekit-users.slack.com/archives/C07FY8WHGPM/p1730293289.619029 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
m
do i need new thread for each phone user ? ı want to use asıstant apı as base llm , @millions-hair-24956 i want to create 1 asıstant and and and use it in every agent ??
g
To use the Assistant API as the base LLM for multiple phone users, you do not necessarily need a new thread for each user. You can create a single assistant instance and use it across multiple agents. This approach allows you to maintain a consistent conversational context and efficiently manage resources. Here's how you can achieve this: 1. Single Assistant Instance: Create one instance of the Assistant API and configure it to handle multiple sessions. This instance can be shared across different agents, allowing them to access the same assistant logic. 2. Session Management: Use session management to handle different users. Each user can have a unique session ID, which helps maintain separate contexts for each conversation. This way, the assistant can manage multiple conversations simultaneously without mixing up the contexts. 3. Thread Management: While you don't need a new thread for each user, you should ensure that your application can handle concurrent requests efficiently. This might involve using asynchronous programming techniques to manage multiple connections. Here's a conceptual code snippet to illustrate this setup:
Copy code
import asyncio
from livekit import agents, rtc
from livekit.plugins import deepgram, openai, silero

# Create a single Assistant API instance
assistant_llm = openai.AssistantLLM(
    assistant_opts=openai.AssistantOptions(
        load_options=openai.AssistantLoadOptions(
            assistant_id="your_assistant_id",
            thread_id=None  # Use None to allow dynamic session management
        )
    )
)

async def entrypoint(ctx: agents.JobContext):
    await ctx.connect()
    print(f"Connected to room: {ctx.room.name}")

    # Initialize the chat context for each session
    chat_context = agents.llm.ChatContext(
        messages=[
            agents.llm.ChatMessage(
                role="system",
                content="You are a helpful assistant."
            )
        ]
    )

    # Initialize the VoicePipelineAgent for each user
    agent = agents.VoicePipelineAgent(
        vad=silero.VAD.load(),
        stt=deepgram.STT(model="nova-2"),
        llm=assistant_llm,
        tts=openai.TTS(),
        chat_ctx=chat_context,
        allow_interruptions=True,
        interrupt_speech_duration=0.5,
        interrupt_min_words=0,
        min_endpointing_delay=0.5,
        preemptive_synthesis=True
    )

    # Start the agent
    agent.start(ctx.room)

    # Keep the connection alive
    while ctx.room.connection_state == rtc.ConnectionState.CONN_CONNECTED:
        await asyncio.sleep(1)

if __name__ == "__main__":
    agents.cli.run_app(agents.WorkerOptions(entrypoint_fnc=entrypoint))
In this setup, the 
thread_id
is set to 
None
to allow dynamic session management. Each user session can be managed separately, ensuring that the assistant can handle multiple conversations concurrently. Would you like more details on any specific part of this setup? Sources: - https://livekit-users.slack.com/archives/C07FY8WHGPM/p1732860048.913659 - https://docs.livekit.io/agents/overview/ 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
m
so good , iwant to use ıt my rag annoy . give me a code snippet i need extra low latency for phone calls . @millions-hair-24956
g
To integrate the Assistant API with RAG using Annoy for low-latency phone calls, you can configure the VoicePipelineAgent to use both the Assistant API and the RAG plugin. This setup will allow you to efficiently handle conversational tasks and retrieve relevant information quickly. Here's a code snippet to illustrate this:
Copy code
import asyncio
from livekit import agents, rtc
from livekit.plugins import deepgram, openai, silero, rag

# Create a single Assistant API instance
assistant_llm = openai.AssistantLLM(
    assistant_opts=openai.AssistantOptions(
        load_options=openai.AssistantLoadOptions(
            assistant_id="your_assistant_id",
            thread_id=None  # Use None to allow dynamic session management
        )
    )
)

# Initialize the RAG plugin with Annoy
rag_plugin = rag.RAG(
    index_path="path_to_your_annoy_index",
    embedding_model=openai.EmbeddingModel()
)

async def entrypoint(ctx: agents.JobContext):
    await ctx.connect()
    print(f"Connected to room: {ctx.room.name}")

    # Initialize the chat context for each session
    chat_context = agents.llm.ChatContext(
        messages=[
            agents.llm.ChatMessage(
                role="system",
                content="You are a helpful assistant."
            )
        ]
    )

    # Initialize the VoicePipelineAgent for each user
    agent = agents.VoicePipelineAgent(
        vad=silero.VAD.load(),
        stt=deepgram.STT(model="nova-2"),
        llm=assistant_llm,
        tts=openai.TTS(),
        chat_ctx=chat_context,
        allow_interruptions=True,
        interrupt_speech_duration=0.3,  # Adjust for lower latency
        interrupt_min_words=1,
        min_endpointing_delay=0.2,  # Adjust for lower latency
        preemptive_synthesis=True,
        rag=rag_plugin  # Integrate RAG with the agent
    )

    # Start the agent
    agent.start(ctx.room)

    # Keep the connection alive
    while ctx.room.connection_state == rtc.ConnectionState.CONN_CONNECTED:
        await asyncio.sleep(1)

if __name__ == "__main__":
    agents.cli.run_app(agents.WorkerOptions(entrypoint_fnc=entrypoint))
In this setup: - The 
assistant_llm
is configured to use the Assistant API, allowing for dynamic session management. - The 
rag_plugin
is initialized with an Annoy index for efficient retrieval. - Parameters like 
interrupt_speech_duration
and 
min_endpointing_delay
are adjusted for lower latency, which is crucial for phone call scenarios. This configuration should help you achieve low-latency interactions for phone calls using RAG with Annoy. Would you like more details on any specific part of this setup? Sources: - https://docs.livekit.io/python/livekit/agents/pipeline/pipeline_agent.html#args - https://docs.livekit.io/python/livekit/agents/pipeline/index.html#args 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
m
assistant_llm = openai.AssistantLLM( assistant_opts=openai.AssistantOptions( load_options=openai.AssistantLoadOptions( assistant_id="your_assistant_id", thread_id=None # Use None to allow dynamic session management ) ) ) there is no parameter like openai.AssistantLLM in livekit plugın openai . @millions-hair-24956
g
It seems that the 
openai.AssistantLLM
is not explicitly mentioned in the provided data sources. If this specific class or method is not available in the LiveKit OpenAI plugin, you might need to use a different approach to integrate the Assistant API with your VoicePipelineAgent. Instead, you can use the 
openai.LLM
class to configure your LLM with the necessary parameters for your application. Here's an alternative approach using the 
openai.LLM
class:
Copy code
import asyncio
from livekit import agents, rtc
from livekit.plugins import deepgram, openai, silero, rag

# Configure the LLM with the necessary parameters
gpt_llm = openai.LLM(
    model="gpt-3.5-turbo",
    api_key="your_api_key",
    streaming=True  # Enable streaming for lower latency
)

# Initialize the RAG plugin with Annoy
rag_plugin = rag.RAG(
    index_path="path_to_your_annoy_index",
    embedding_model=openai.EmbeddingModel()
)

async def entrypoint(ctx: agents.JobContext):
    await ctx.connect()
    print(f"Connected to room: {ctx.room.name}")

    # Initialize the chat context for each session
    chat_context = agents.llm.ChatContext(
        messages=[
            agents.llm.ChatMessage(
                role="system",
                content="You are a helpful assistant."
            )
        ]
    )

    # Initialize the VoicePipelineAgent for each user
    agent = agents.VoicePipelineAgent(
        vad=silero.VAD.load(),
        stt=deepgram.STT(model="nova-2"),
        llm=gpt_llm,
        tts=openai.TTS(),
        chat_ctx=chat_context,
        allow_interruptions=True,
        interrupt_speech_duration=0.3,  # Adjust for lower latency
        interrupt_min_words=1,
        min_endpointing_delay=0.2,  # Adjust for lower latency
        preemptive_synthesis=True,
        rag=rag_plugin  # Integrate RAG with the agent
    )

    # Start the agent
    agent.start(ctx.room)

    # Keep the connection alive
    while ctx.room.connection_state == rtc.ConnectionState.CONN_CONNECTED:
        await asyncio.sleep(1)

if __name__ == "__main__":
    agents.cli.run_app(agents.WorkerOptions(entrypoint_fnc=entrypoint))
In this setup, the 
openai.LLM
class is used to configure the LLM with streaming enabled for lower latency. The RAG plugin is integrated with Annoy for efficient retrieval. Would you like more details on any specific part of this setup? Sources: - LiveKit documentation - LiveKit documentation 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
m
class *OnFileUploadedInfo*(type: "Literal['image']",
original_file: llm.ChatImage, openai_file_object: FileObject) Expand source code
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@dataclass
class OnFileUploadedInfo:
    type: Literal["image"]
    original_file: llm.ChatImage
    openai_file_object: FileObject
OnFileUploadedInfo(type: "Literal['image']", original_file: 'llm.ChatImage', openai_file_object: 'FileObject')
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i found it ! but  s OnFileUploadedInfo:
    type: Literal["image"]
we need to give is as text ? @millions-hair-24956
g
The 
OnFileUploadedInfo
class is a data structure used to represent information about an uploaded file, specifically an image in this context. The 
type
attribute is a 
Literal
type, which means it is expected to be a specific string value, in this case, 
"image"
. This is not something you need to provide as text; rather, it is a type hint indicating that the 
type
attribute should always be the string 
"image"
. Here's a breakdown of the class attributes: - 
type
: This is a 
Literal
type, which means it should always be set to 
"image"
. This is used to ensure that the type of file being handled is an image. - 
original_file
: This is expected to be an instance of 
llm.ChatImage
, representing the original image file that was uploaded. - 
openai_file_object
: This is expected to be an instance of 
FileObject
, which likely contains metadata or a reference to the file as it exists in the OpenAI system. You don't need to provide the 
type
as text; it is a fixed value that indicates the kind of file being handled. When you create an instance of 
OnFileUploadedInfo
, you will set the 
original_file
and 
openai_file_object
attributes with the appropriate objects. Would you like more details on how to use this class in your application? Sources: - https://docs.livekit.io/python/livekit/plugins/openai/beta/index.html#livekit.plugins.openai.beta.AssistantLLM.chat - https://docs.livekit.io/python/livekit/plugins/openai/beta/assistant_llm.html#livekit.plugins.openai.beta.assistant_llm.AssistantLLMStream.EventHandler 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
m
we want to use azure openai assistant api , is it possible ? @millions-hair-24956
g
Yes, it is possible to use the Azure OpenAI Assistant API with LiveKit. You can configure your application to interact with Azure's OpenAI services by setting up the necessary parameters such as the Azure deployment name, endpoint, API version, and authentication credentials. To integrate Azure OpenAI with LiveKit, you would typically use the 
with_azure
method provided by the LiveKit SDK. This method allows you to configure a RealtimeClient instance for Azure OpenAI Service. Here’s a basic outline of how you might set this up:
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from livekit.plugins.openai.realtime import RealtimeModel

# Configure the RealtimeModel with Azure settings
realtime_model = RealtimeModel.with_azure(
    azure_deployment="your_azure_deployment_name",
    azure_endpoint="your_azure_endpoint",
    api_version="your_api_version",
    api_key="your_azure_api_key",
    entra_token=None,  # Use if not using API key authentication
    base_url=None,  # Optional, constructed from the azure_endpoint if not provided
    instructions="You are a helpful assistant.",
    modalities=["text", "audio"],
    voice="alloy",
    input_audio_format="pcm16",
    output_audio_format="pcm16"
)

# Use the configured model in your application
Make sure to replace placeholders like 
"your_azure_deployment_name"
, 
"your_azure_endpoint"
, and 
"your_azure_api_key"
with your actual Azure OpenAI configuration details. Would you like more detailed guidance on setting up Azure OpenAI with LiveKit? Sources: - LiveKit RealtimeModel with Azure 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
m
but i need azure assistant api not realtime @millions-hair-24956
g
It seems that the Azure Assistant API is not explicitly mentioned in the provided data sources. If you're looking to use Azure's Assistant API specifically, and it's not covered by the existing LiveKit plugins or documentation, you might need to implement a custom integration. Here's a general approach you could take to integrate Azure's Assistant API with LiveKit: 1. Azure API Configuration: Set up your Azure environment to access the Assistant API. This typically involves obtaining the necessary API keys, endpoint URLs, and any other required credentials. 2. Custom Integration: Implement a custom integration in your application to interact with the Azure Assistant API. This would involve making HTTP requests to the Azure API endpoints and handling the responses. 3. LiveKit Integration: Use LiveKit's framework to manage the media and data exchange. You can create a custom agent that interacts with the Azure Assistant API for processing conversational tasks. Here's a conceptual code snippet to illustrate how you might start setting up a custom integration:
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import requests

# Define your Azure Assistant API endpoint and credentials
azure_endpoint = "https://your-azure-endpoint"
api_key = "your_azure_api_key"

def call_azure_assistant_api(prompt):
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }
    data = {
        "prompt": prompt,
        "max_tokens": 150
    }
    response = requests.post(azure_endpoint, headers=headers, json=data)
    return response.json()

# Example usage
response = call_azure_assistant_api("Hello, how can I assist you today?")
print(response)
This example demonstrates how to make a basic request to an Azure API endpoint. You would need to adapt this to fit the specific requirements and capabilities of the Azure Assistant API. Would you like more detailed guidance on any specific part of this setup? Sources: - https://livekit-users.slack.com/archives/C07FY8WHGPM/p1726170916.058479 - https://docs.livekit.io/python/livekit/plugins/openai/index.html#livekit.plugins.openai.LLM.create_azure_client Note: I wasn’t able to find highly relevant data sources, but above are a few potentially relevant links. 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
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