#openai-realtime-console

1 messages · Page 1 of 1 (latest)

vast obsidianBOT
#

Hi @devout briar. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:

#

Yes, you can create a chatbot using the OpenAI Realtime API, and the openai-realtime-console can be a helpful tool in this process. The Realtime API allows you to build low-latency, multi-modal conversational experiences with expressive voice-enabled models. You can use the Realtime console sample app to exercise function calling, client and server events, and more, which are essential components for building a chatbot.

To get started, you can explore the Realtime Console sample app, which demonstrates how to interact with the Realtime API. This console can help you understand how to manage sessions, send and receive events, and handle model responses, which are crucial for developing a chatbot.

For more detailed guidance on using the Realtime API, you can refer to the Realtime API documentation and the Realtime model capabilities guide.

If you need to implement specific functionalities, such as text and audio inputs and outputs, you can refer to the Text inputs and outputs guide for code examples and instructions.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@devout briar Kapa.ai is still learning and improving, please let me know how I did by reacting below.

devout briar
#

@vast obsidian i want only for chat not for audio can you provide me code for that

vast obsidianBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

To create a text-only chatbot using the OpenAI Realtime API, you can configure your session to handle only text inputs and outputs. Below is a Python example that demonstrates how to set up a text-based conversation using the Realtime API:

import openai

# 1. Create or configure your OpenAI client (assuming you have an API key).
client = openai.OpenAI(api_key="YOUR_API_KEY")

# 2. Optionally create a session with some configuration if you want:
session = client.beta.realtime.sessions.create(
    model="gpt-4o-realtime-preview",
    instructions="You are a friendly assistant. Respond with short answers.",
    modalities=["text"],  # We only want text responses for this example
)

# 3. Open a Realtime WebSocket connection
with client.beta.realtime.connect(model="gpt-4o-realtime-preview") as connection:
    # 4. (Optional) Immediately update session config if you want
    connection.session.update(session={
        "instructions": "Please respond in very short phrases.",
        # other fields you want to override, like temperature, etc.
    })

    # 5. Send a user message into the conversation
    connection.conversation.item.create(item={
        "type": "user", 
        "text": "Hello! How are you?"
    })

    # 6. Instruct the model to create a response
    connection.response.create()

    # 7. Receive events. We'll read until we get the final "response.done" event.
    for event in connection:
        print(f"Received event: {event.type}")
        if event.type == "conversation.item.created":
            # Might contain the text or partial text from the assistant
            if event.item and event.item.type == "assistant" and event.item.text:
                print("Assistant says:", event.item.text)

        if event.type == "response.done":
            # The model finished replying
            break
#

This code sets up a Realtime session configured for text-only responses. It sends a user message and waits for the model to generate a response, printing the assistant's reply when received. You can adjust the session configuration and message content as needed for your chatbot application.

For more information, you can refer to the Realtime model capabilities guide.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@devout briar Kapa.ai is still learning and improving, please let me know how I did by reacting below.

devout briar
#

@vast obsidian what about in frontend like not in backend we are gave everything in fromtend just like we saw demon of openai-realtime-console we are in / Initiate chat
const initiateChat = async() => {
console.log("initiateChat")
setisTestCall(false)
setisWebCallActive(true);
setisAnimateWebCallBtn(true);
try{
const data = await getRealtimeSessionData();
const pc = new RTCPeerConnection();
pcRef.current = pc;

  const audioContext = new AudioContext();
  const oscillator = audioContext.createOscillator();
  const dst = oscillator.connect(audioContext.createMediaStreamDestination());
  oscillator.start();
  const track = dst.stream.getAudioTracks()[0];
  pc.addTrack(track);

  // Set up data channel
  const dc = pc.createDataChannel("oai-events");
  dc.onopen = () => {
    console.log("Data channel is open");
  };

  dc.onclose = () => {
    console.log("Data channel is closed");
  };

  dc.onerror = (error) => {
    console.error('Data channel error:', error);
  };

  dc.addEventListener("message", (e) => {
    const realtimeEvent = JSON.parse(e.data);
    switch (realtimeEvent.type) {

      case 'response.output_item.done':
        console.log("response.output_item.done")
        console.log(realtimeEvent.item.content)
        if(realtimeEvent.item.content[0].transcript != null){
          setMessages((prevMessages) => [...prevMessages, { 
            message: realtimeEvent.item.content[0].transcript, 
            role: "AI",
            time: new Date().toLocaleTimeString([], {hour: '2-digit', minute: '2-digit'}).replace("AM","").replace("PM","")
          }]);
        }
        break;
    }
  });
  dcRef.current = dc;
vast obsidianBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

To create a text-only chatbot on the frontend using the OpenAI Realtime API, you can utilize the WebRTC data channel to send and receive text messages. Below is an example of how you might set up a WebRTC connection and handle text-based communication in a frontend application, similar to the openai-realtime-console demo:

// Function to initiate chat
const initiateChat = async () => {
  console.log("initiateChat");
  setisTestCall(false);
  setisWebCallActive(true);
  setisAnimateWebCallBtn(true);

  try {
    const data = await getRealtimeSessionData(); // Function to get session data
    const pc = new RTCPeerConnection();
    pcRef.current = pc;

    // Set up data channel
    const dc = pc.createDataChannel("oai-events");
    dc.onopen = () => {
      console.log("Data channel is open");
    };

    dc.onclose = () => {
      console.log("Data channel is closed");
    };

    dc.onerror = (error) => {
      console.error('Data channel error:', error);
    };

    dc.addEventListener("message", (e) => {
      const realtimeEvent = JSON.parse(e.data);
      switch (realtimeEvent.type) {
        case 'response.output_item.done':
          console.log("response.output_item.done");
          console.log(realtimeEvent.item.content);
          if (realtimeEvent.item.content[0].transcript != null) {
            setMessages((prevMessages) => [...prevMessages, { 
              message: realtimeEvent.item.content[0].transcript, 
              role: "AI",
              time: new Date().toLocaleTimeString([], {hour: '2-digit', minute: '2-digit'}).replace("AM","").replace("PM","")
            }]);
          }
          break;
      }
    });
    dcRef.current = dc;
  } catch (error) {
    console.error("Error initiating chat:", error);
  }
};
#

Key Points:

  • Data Channel Setup: The RTCPeerConnection is used to create a data channel named "oai-events", which is responsible for sending and receiving messages.
  • Event Handling: The message event listener on the data channel processes incoming messages. In this example, it checks for the response.output_item.done event type and updates the chat messages accordingly.
  • Session Data: The getRealtimeSessionData() function is assumed to fetch necessary session data, such as an ephemeral token, to establish the connection.

This setup allows you to handle text-based interactions with the Realtime API directly from the frontend, leveraging WebRTC for real-time communication. For more detailed guidance, you can refer to the Realtime API documentation.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@devout briar Kapa.ai is still learning and improving, please let me know how I did by reacting below.

devout briar
#

@vast obsidian this is the continue of above code please look into this also // Start the session using SDP
const offer = await pc.createOffer();
await pc.setLocalDescription(offer);

  const baseUrl = "https://api.openai.com/v1/realtime";
  const sdpResponse = await fetch(`${baseUrl}?model=${data.model}`, {
    method: "POST",
    body: offer.sdp,
    headers: {
      Authorization: `Bearer ${data.client_secret.value}`,
      "Content-Type": "application/sdp",
    }
  });

  const answer = { type: "answer", sdp: await sdpResponse.text() };
  await pc.setRemoteDescription(answer);
}catch (error) {
  console.error("Error initializing Web call", error);
}

}

// Add a method to send text input
const sendTextInput = async (e) => {
e.preventDefault();
try {
if (dcRef.current.readyState === 'open') {
const textEvent = {
type: "conversation.item.create",
item: {
type: "message",
role: "user",
content: [
{
type: "input_text",
text: message,
modality: "text"
},
],
}
}
textEvent.event_id = textEvent.event_id || crypto.randomUUID();
console.log('Data channel state before send textEvent:', dcRef.current.readyState);
dcRef.current.send(JSON.stringify(textEvent));
console.log('Data channel state after send textEvent:', dcRef.current.readyState);

    const responseEvent = { type: "response.create" }
    responseEvent.event_id = responseEvent.event_id || crypto.randomUUID();
    console.log('Data channel state before send responseEvent:', dcRef.current.readyState);
    dcRef.current.send(JSON.stringify(responseEvent));
    console.log('Data channel state after send responseEvent:', dcRef.current.readyState);
vast obsidianBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

To continue building a text-only chatbot using the OpenAI Realtime API on the frontend, you can use the following code to handle the WebRTC connection and send text inputs over the data channel. This code snippet is a continuation of the previous setup and includes methods for starting the session using SDP and sending text input:

// Start the session using SDP
const offer = await pc.createOffer();
await pc.setLocalDescription(offer);

const baseUrl = "<https://api.openai.com/v1/realtime";>
const sdpResponse = await fetch(`${baseUrl}?model=${data.model}`, {
  method: "POST",
  body: offer.sdp,
  headers: {
    Authorization: `Bearer ${data.client_secret.value}`,
    "Content-Type": "application/sdp",
  }
});

const answer = { type: "answer", sdp: await sdpResponse.text() };
await pc.setRemoteDescription(answer);

// Add a method to send text input
const sendTextInput = async (e) => {
  e.preventDefault();
  try {
    if (dcRef.current.readyState === 'open') {
      const textEvent = {
        type: "conversation.item.create",
        item: {
          type: "message",
          role: "user",
          content: [
            {
              type: "input_text",
              text: message,
              modality: "text"
            },
          ],
        }
      };
      textEvent.event_id = textEvent.event_id || crypto.randomUUID();
      console.log('Data channel state before send textEvent:', dcRef.current.readyState);
      dcRef.current.send(JSON.stringify(textEvent));
      console.log('Data channel state after send textEvent:', dcRef.current.readyState);
#

const responseEvent = { type: "response.create" };
responseEvent.event_id = responseEvent.event_id || crypto.randomUUID();
console.log('Data channel state before send responseEvent:', dcRef.current.readyState);
dcRef.current.send(JSON.stringify(responseEvent));
console.log('Data channel state after send responseEvent:', dcRef.current.readyState);
}
} catch (error) {
console.error("Error sending text input", error);
}
};


### Key Points:
- **SDP Offer/Answer**: The code creates an SDP offer and sends it to the OpenAI API to establish a WebRTC connection. The response is used to set the remote description.
- **Data Channel**: The `sendTextInput` function sends a text message over the data channel. It creates a `conversation.item.create` event for the user message and a `response.create` event to request a response from the model.
- **Event IDs**: Each event is assigned a unique `event_id` using `crypto.randomUUID()` to ensure proper tracking and handling of events.

This setup allows you to send and receive text messages in real-time using the Realtime API, enabling the creation of a text-only chatbot on the frontend. For more details, you can refer to the [Realtime API documentation](<https://platform.openai.com/docs/guides/realtime-webrtc#sending-and-receiving-events>).
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
#

@devout briar Kapa.ai is still learning and improving, please let me know how I did by reacting below.

devout briar
#

@vast obsidian and this is also continue of the above code textEvent.event_id = textEvent.event_id || crypto.randomUUID();
console.log('Data channel state before send textEvent:', dcRef.current.readyState);
dcRef.current.send(JSON.stringify(textEvent));
console.log('Data channel state after send textEvent:', dcRef.current.readyState);

    const responseEvent = { type: "response.create" }
    responseEvent.event_id = responseEvent.event_id || crypto.randomUUID();
    console.log('Data channel state before send responseEvent:', dcRef.current.readyState);
    dcRef.current.send(JSON.stringify(responseEvent));
    console.log('Data channel state after send responseEvent:', dcRef.current.readyState);

    setMessages((prevMessages) => [...prevMessages, {
       message: message, 
       role: "You",
       time: new Date().toLocaleTimeString([], {hour: '2-digit', minute: '2-digit'}).replace("AM","").replace("PM","") 
    }]);
    setMessage("");
  }
} catch (error) {
  console.log(error)      
}

};

// End chat
const endChat = async() => {
try{
if (pcRef.current) {
pcRef.current.close();
pcRef.current = null;
setIsCallActive(false);
}
}catch (error) {
console.error("Error ending chat", error);
}
}

// Get realtime session from open ai
const getRealtimeSessionData = async() => {
try{
const baseURL = import.meta.env.VITE_BASE_API_URL;
const tokenResponse = await fetch(baseURL+"/oa/getRealtimeSession?self=self&agent_id="+agentId,{
headers: {
Authorization: Bearer ${user.token},
"Content-Type": "application/json",
}
});
const data = await tokenResponse.json();
const EPHEMERAL_KEY = data.client_secret.value;
return data;
}catch (error) {
console.error("Error getRealtimeSessionData", error);
}
}

vast obsidianBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

To complete the setup for a text-only chatbot using the OpenAI Realtime API on the frontend, you can use the following code to manage the chat session, send messages, and handle the end of the chat. This code snippet continues from the previous setup and includes methods for sending text input, ending the chat, and retrieving session data:

// Send text input
const sendTextInput = async (e) => {
  e.preventDefault();
  try {
    if (dcRef.current.readyState === 'open') {
      const textEvent = {
        type: "conversation.item.create",
        item: {
          type: "message",
          role: "user",
          content: [
            {
              type: "input_text",
              text: message,
              modality: "text"
            },
          ],
        }
      };
      textEvent.event_id = textEvent.event_id || crypto.randomUUID();
      console.log('Data channel state before send textEvent:', dcRef.current.readyState);
      dcRef.current.send(JSON.stringify(textEvent));
      console.log('Data channel state after send textEvent:', dcRef.current.readyState);

      const responseEvent = { type: "response.create" };
      responseEvent.event_id = responseEvent.event_id || crypto.randomUUID();
      console.log('Data channel state before send responseEvent:', dcRef.current.readyState);
      dcRef.current.send(JSON.stringify(responseEvent));
      console.log('Data channel state after send responseEvent:', dcRef.current.readyState);

      setMessages((prevMessages) => [...prevMessages, {
        message: message, 
        role: "You",
        time: new Date().toLocaleTimeString([], {hour: '2-digit', minute: '2-digit'}).replace("AM","").replace("PM","") 
      }]);
      setMessage("");
    }
  } catch (error) {
    console.log(error);
  }
};
#

// End chat
const endChat = async () => {
try {
if (pcRef.current) {
pcRef.current.close();
pcRef.current = null;
setIsCallActive(false);
}
} catch (error) {
console.error("Error ending chat", error);
}
};

// Get realtime session from OpenAI
const getRealtimeSessionData = async () => {
try {
const baseURL = import.meta.env.VITE_BASE_API_URL;
const tokenResponse = await fetch(baseURL + "/oa/getRealtimeSession?self=self&agent_id=" + agentId, {
headers: {
Authorization: Bearer ${user.token},
"Content-Type": "application/json",
}
});
const data = await tokenResponse.json();
const EPHEMERAL_KEY = data.client_secret.value;
return data;
} catch (error) {
console.error("Error getRealtimeSessionData", error);
}
};


### Key Points:
- **Sending Messages**: The `sendTextInput` function sends a user message and requests a response from the model. It updates the chat messages with the user's input.
- **Ending the Chat**: The `endChat` function closes the WebRTC connection and resets the call state.
- **Session Data Retrieval**: The `getRealtimeSessionData` function fetches session data, including an ephemeral key, necessary for establishing a connection with the OpenAI Realtime API.

This setup allows you to manage a text-only chat session on the frontend, leveraging the Realtime API for real-time communication. For more details, you can refer to the [Realtime API documentation](<https://platform.openai.com/docs/guides/realtime-webrtc#sending-and-receiving-events>).
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
#

@devout briar Kapa.ai is still learning and improving, please let me know how I did by reacting below.

devout briar
#

@vast obsidian i shared you complete code know i am facing this error initiateChat
@sentry_react.js?v=fbf2af06:6267 Data channel is open
@sentry_react.js?v=fbf2af06:6267 Data channel state before send textEvent: open
@sentry_react.js?v=fbf2af06:6267 Data channel state after send textEvent: open
@sentry_react.js?v=fbf2af06:6267 Data channel state before send responseEvent: open
@sentry_react.js?v=fbf2af06:6267 Data channel state after send responseEvent: open
@sentry_react.js?v=fbf2af06:6267 Data channel error: RTCErrorEvent
(anonymous) @ @sentry_react.js?v=fbf2af06:6267Understand this errorAI
@sentry_react.js?v=fbf2af06:6267 Data channel is closed

vast obsidianBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

The error you're encountering, RTCErrorEvent, followed by the data channel closing, suggests that there might be an issue with the WebRTC connection or the data being sent over the channel. Here are a few steps you can take to troubleshoot and potentially resolve this issue:

  1. Check SDP Offer/Answer Exchange: Ensure that the SDP offer/answer exchange is completed successfully. Any issues in this process can lead to connection problems. Verify that the setLocalDescription and setRemoteDescription calls are successful and that the SDP data is correctly formatted.

  2. Validate Data Channel Configuration: Make sure that the data channel is configured correctly. Check for any constraints or parameters that might be causing issues. The data channel should be set up with the correct label and options.

  3. Inspect the Data Being Sent: Ensure that the data being sent over the data channel is correctly formatted and not too large. JSON.stringify should be used to serialize the data, and you should check for any potential issues with the data structure.

  4. Error Handling: Add more detailed error handling to capture and log specific errors. This can help identify the root cause of the RTCErrorEvent. For example, you can log the error message and any associated data:

    dc.onerror = (error) => {
  console.error('Data channel error:', error.message, error);
};

  5. Network Conditions: Consider testing under different network conditions to see if the issue persists. Sometimes network instability can cause WebRTC connections to fail.

  6. Review Browser Console: Check the browser console for any additional error messages or warnings that might provide more context about the issue.

#

If these steps do not resolve the issue, you may need to consult the WebRTC documentation for more detailed guidance on handling WebRTC connections and data channels. Additionally, reviewing the OpenAI Realtime API documentation might provide further insights into any specific requirements or constraints related to the API.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon: