----------------------------- **While I generate an answer, here are some sources that might be helpful**: * [Server Events]() * [Create Assistant]() * [Create Phone Call]() * [Get Call]() -----------------------------

### Answer To integrate your serverless function with Vapi, you can utilize the [Server Events]() feature to handle various types of messages, such as function calls, during a conversation. You can configure your Vapi assistant to call your serverless function by specifying its URL in the assistant's settings or dynamically during a conversation. Here's a simplified approach to integrate your existing function with Vapi: 1. Configure Server URL in Vapi: - Set the server URL to point to your serverless function's endpoint. This can be done in the assistant's configuration on the Vapi Dashboard. 2. Adapt Your Function for Vapi: - Modify your function to handle requests from Vapi. For instance, you might want to process

function-call

messages which Vapi sends when it wants to trigger an action on your server [(1)]() . 3. Respond to Vapi: - Ensure your function sends a response back to Vapi in the expected format. If your function was triggered by a

function-call

, the response should contain the results of the function call, which Vapi can then use in the conversation [(2)]() . Here's an example of how you could modify your function to work with Vapi, based on handling a `function-call`:

python
from flask import Flask, request, jsonify
import requests

app = Flask(__name__)

@app.route("/your-function-endpoint", methods=["POST"])
def vapi_function():
    data = request.get_json()
    # Assuming 'data' contains a 'function-call' from Vapi
    function_name = data['call']['functionCall']['name']
    
    if function_name == "your_function_name_here":
        # Process the call
        response_data = {'nom': 'X Y'}
        response = requests.post("https://function/", json=response_data)
        
        # Prepare the response for Vapi
        vapi_response = {
            "results": [
                {
                    "toolCallId": data['call']['id'],
                    "result": response.json()
                }
            ]
        }
        return jsonify(vapi_response)

    return jsonify({"error": "Function not recognized"}), 400

if __name__ == "__main__":
    app.run(debug=True)

[(1)]() , [(2)]() Remember, you'll need to adapt the

"your_function_name_here"

and the endpoint

"/your-function-endpoint"

to match your configuration in Vapi. This example assumes you're using Flask for your serverless function and that you've already set up the necessary configurations in Vapi to call this function.

----------------------------- **While I generate an answer, here are some sources that might be helpful**: * [Connecting Your Custom LLM to Vapi: A Comprehensive Guide]() * [Create Assistant]() * [Tools Calling]() * [Get Assistant]() -----------------------------

### Answer To make a POST request to your FastAPI endpoint from Vapi, you can utilize Vapi's capability to call external functions during an interaction with your voice AI agent. This is typically done by configuring a tool within your Vapi assistant that specifies when and how to call your external function. You'll want to set this up in the "tools" section of your assistant configuration. Here's a general approach, adapted for use with Vapi: 1. Define the Tool in Vapi: - You'll need to define a tool in your Vapi assistant configuration that specifies the action to call your FastAPI endpoint. Tools are designed to interact with external services and can be configured to make HTTP requests. 2. Tool Configuration: - Within your tool configuration, specify the type as "function", provide a name, and set the server URL to your FastAPI endpoint. You can also define messages that the AI will communicate to the user based on the tool's execution stages (e.g., "request-start", "request-complete"). 3. Server URL Configuration: - Although you have your FastAPI endpoint, you'll still need to configure the Server URL in Vapi to handle outgoing requests [(1)]() . This Server URL will be where Vapi sends events and where it expects to receive instructions for making external function calls, including to your FastAPI service. Here's an abstracted example based on Vapi's documentation:

json
{
  "type": "function",
  "messages": [
    {
      "type": "request-start",
      "content": "Initiating action..."
    },
    {
      "type": "request-complete",
      "content": "Action completed."
    }
  ],
  "function": {
    "name": "call_my_fastapi_function",
    "server": {
      "url": "https://your-fastapi-endpoint-url.com/path"
    }
  },
  "async": false
}

4. Handle the Response: - Your FastAPI application should return a response in a format that Vapi expects, especially if the call to your function is intended to influence the ongoing interaction. Ensure your FastAPI endpoint sends back a JSON response that aligns with what Vapi tools can process. This approach leverages Vapi’s ability to integrate with external systems through configured tools and server interactions. You'll need to adjust the specifics based on your assistant's needs and the details of your FastAPI application.

----------------------------- **While I generate an answer, here are some sources that might be helpful**: * [Server Events]() * [Vapi Flutter SDK]() * [Function Calling]() * [Web SDK]() -----------------------------

### Answer To send data to your function when it's called by Vapi, you can utilize custom functions within your assistant's configuration. This involves defining the function in your assistant and specifying the parameters that need to be sent to your endpoint. When Vapi calls your function, it can send data as parameters in the request. You define these parameters when setting up your custom function in the assistant's configuration. Here’s a conceptual example based on Vapi's capabilities for function calling:

json
{
  "functions": [
    {
      "name": "myCustomFunction",
      "description": "Send data to my endpoint for processing.",
      "parameters": {
        "type": "object",
        "properties": {
          "nom": {
            "type": "string",
            "description": "A name to be processed by the function."
          },
          // Include other parameters here as needed
        }
      }
    }
  ]
}

In this configuration, you define a custom function named

myCustomFunction

that expects data, such as a name (

nom

), to be sent to it. When setting up the call to this function within your conversation flow or assistant logic, you would specify the actual values for these parameters based on the conversation context or static values as needed. This JSON configuration is conceptual and needs to be adapted to your specific setup with Vapi and how you've structured your assistant and endpoint. The key is to ensure that your FastAPI endpoint is prepared to accept these parameters from Vapi and process them accordingly. Remember to specify the URL where Vapi should send these function calls, which would be your FastAPI endpoint, in the tool or function configuration on the Vapi platform. For detailed instructions on setting up custom functions and sending data to them, refer to Vapi's documentation on [Function Calling]() [(1)]() and [Tools Calling]() [(2)]() . These sections provide insights into how to structure your requests and configure your assistant to interact with external services like your FastAPI endpoint.

----------------------------- **While I generate an answer, here are some sources that might be helpful**: * [Connecting Your Custom LLM to Vapi: A Comprehensive Guide]() * [Prompting Guide]() * [Tools Calling]() * [Web Snippet]() -----------------------------

### Answer Sorry, I wasn't able to find any information about your question. You can get more help at [Contact Us](support@vapi.ai)