added documentation for customLLM.

Signed-off-by: Sahil Suman <sahilsuman933@gmail.com>

Author: sahilsuman933

custom-llm-guide.mdx

@@ -0,0 +1,87 @@
+---
+title: "Connecting Your Custom LLM to Vapi: A Comprehensive Guide"
+sidebarTitle: "Custom LLM"
+---
+
+This guide provides a comprehensive walkthrough on integrating Vapi with OpenAI's gpt-3.5-turbo-instruct model using a custom LLM configuration. We'll leverage Ngrok to expose a local development environment for testing and demonstrate the communication flow between Vapi and your LLM.
+## Prerequisites
+
+- **Vapi Account**: Access to the Vapi Dashboard for configuration.
+- **OpenAI API Key**: With access to the gpt-3.5-turbo-instruct model.
+- **Python Environment**: Set up with the OpenAI library (`pip install openai`).
+- **Ngrok**: For exposing your local server to the internet.
+- **Code Reference**: Familiarize yourself with the `/openai-sse/chat/completions` endpoint function in the provided Github repository: [Server-Side Example Python Flask](https://github.com/VapiAI/server-side-example-python-flask/blob/main/app/api/custom_llm.py).
+
+## Step 1: Setting Up Your Local Development Environment
+
+**1. Create a Python Script (app.py):**
+
+```python
+from flask import Flask, request, jsonify
+import openai
+
+app = Flask(__name__)
+openai.api_key = "YOUR_OPENAI_API_KEY"  # Replace with your actual API key
+
+@app.route("/chat/completions", methods=["POST"])
+def chat_completions():
+    data = request.get_json()
+    # Extract relevant information from data (e.g., prompt, conversation history)
+    # ...
+    
+    response = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo-instruct",
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant."},
+            # ... (Add messages from conversation history and current prompt)
+        ]
+    )
+    # Format response according to Vapi's structure
+    # ...
+    return jsonify(formatted_response)
+
+if __name__ == "__main__":
+    app.run(debug=True, port=5000)  # You can adjust the port if needed
+```
+**2. Run the Script:**
+Execute the Python script using python app.py in your terminal. This will start the Flask server on the specified port (5000 in this example).
+
+**3. Expose with Ngrok:**
+Open a new terminal window and run ngrok http 5000 (replace 5000 with your chosen port) to create a public URL that tunnels to your local server.
+
+## Step 2: Configuring Vapi with Custom LLM
+**1. Access Vapi Dashboard:**
+Log in to your Vapi account and navigate to the "Model" section.
+
+**2. Select Custom LLM:**
+Choose the "Custom LLM" option to set up the integration.
+
+**3. Enter Ngrok URL:**
+Paste the public URL generated by ngrok (e.g., https://your-unique-id.ngrok.io) into the endpoint field. This will be the URL Vapi uses to communicate with your local server.
+
+**4. Test the Connection:**
+Send a test message through the Vapi interface to ensure it reaches your local server and receives a response from the OpenAI API. Verify that the response is displayed correctly in Vapi.
+
+## Step 3: Understanding the Communication Flow
+**1. Vapi Sends POST Request:**
+When a user interacts with your Vapi application, Vapi sends a POST request containing conversation context and metadata to the configured endpoint (your ngrok URL).
+
+**2. Local Server Processes Request:**
+Your Python script receives the POST request and the chat_completions function is invoked.
+
+**3. Extract and Prepare Data:**
+The script parses the JSON data, extracts relevant information (prompt, conversation history), and builds the prompt for the OpenAI API call.
+
+**4. Call to OpenAI API:**
+The constructed prompt is sent to the gpt-3.5-turbo-instruct model using the openai.ChatCompletion.create method.
+
+**5. Receive and Format Response:**
+The response from OpenAI, containing the generated text, is received and formatted according to Vapi's expected structure.
+
+**6. Send Response to Vapi:**
+The formatted response is sent back to Vapi as a JSON object.
+
+**7. Vapi Displays Response:**
+Vapi receives the response and displays the generated text within the conversation interface to the user.
+
+By following these detailed steps and understanding the communication flow, you can successfully connect Vapi to OpenAI's gpt-3.5-turbo-instruct model and create powerful conversational experiences within your Vapi applications. The provided code example and reference serve as a starting point for you to build and customize your integration based on your specific needs.

mint.json

@@ -184,7 +184,8 @@
           "group": "Multilingual Support",
           "pages": ["multilingual"]
         },
-        "GHL"
+        "GHL",
+        "custom-llm-guide"
       ]
     },