> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.vapi.ai/llms.txt. > For full documentation content, see https://docs.vapi.ai/llms-full.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.vapi.ai/_mcp/server. # Documentation agent Try our live implementation using the voice widget in the bottom-right corner of this page. ## Overview Build a voice-powered documentation assistant step by step. Choose between using the Dashboard interface or programmatic APIs to suit your workflow. You'll learn to: * Index your docs with LlamaCloud * Create a RAG tool for document retrieval * Create an assistant with Claude 3.5 Sonnet and attach the tool * Use the Web SDK to create a widget * Analyze user sessions and improve the quality of your agent overtime ## Prerequisites * [Vapi account](https://dashboard.vapi.ai/) with API access * Documentation content - `llms.txt` file ([example](https://docs.vapi.ai/llms.txt)) could work great; it could be available out-of-box with your documentation framework (e.g. [Fern](https://buildwithfern.com/learn/docs/developer-tools/llms-txt), [Mintlify](https://mintlify.com/docs/ai-ingestion#%2Fllms-full-txt)) * [LlamaCloud account](https://cloud.llamaindex.ai/) for indexing ## Get started Upload and index your documentation in LlamaCloud using `text-embedding-3-small`. 1. Create a new project in LlamaCloud 2. Upload your documentation files (you can use a single consolidated file like [llms-full.txt](https://docs.vapi.ai/llms-full.txt)) 3. Configure embedding model to `text-embedding-3-small` 4. Set chunking to 512 tokens with 50 token overlap 5. Note your index ID and API credentials Consolidate your documentation into a single text file for better RAG performance. You can see our example at [docs.vapi.ai/llms-full.txt](https://docs.vapi.ai/llms-full.txt). Create a tool that connects your assistant to your LlamaCloud index. 1. Navigate to **Tools** in your [Vapi Dashboard](https://dashboard.vapi.ai/) 2. Click **Create Tool** 3. Select **API Request** as the tool type 4. Configure the tool: * **Name**: `docsquery` * **Function Name**: `docsquery` * **Description**: `Search through documentation to find relevant information` * **URL**: `https://api.cloud.llamaindex.ai/api/v1/pipelines/YOUR_PIPELINE_ID/retrieve` * **Method**: `POST` * **Headers**: Add `Authorization: Bearer YOUR_LLAMACLOUD_API_KEY` * **Body**: Configure to send the query parameter 5. Save the tool and note the tool ID ```typescript import { VapiClient } from "@vapi-ai/server-sdk"; // Initialize Vapi server SDK const vapi = new VapiClient({ token: "YOUR_VAPI_API_KEY" }); // Create the documentation query tool const tool = await vapi.tools.create({ type: "function", function: { name: "docsquery", parameters: { type: "object", properties: { query: { type: "string", description: "The search query to find relevant documentation" } }, required: ["query"] } }, server: { // LlamaCloud API endpoint for your pipeline url: `https://api.cloud.llamaindex.ai/api/v1/pipelines/${YOUR_PIPELINE_ID}/retrieve`, method: "POST", headers: { "Content-Type": "application/json", "Authorization": `Bearer ${YOUR_LLAMACLOUD_API_KEY}` }, // Send user query to LlamaCloud body: { query: "{{query}}" } } }); console.log(`Tool created with ID: ${tool.id}`); ``` ```python from vapi import Vapi # Initialize Vapi server SDK client = Vapi(token="YOUR_VAPI_API_KEY") # Define system prompt for documentation assistant system_prompt = """You are a helpful documentation assistant. Use the docsquery tool to find relevant information when users ask questions about the documentation. Guidelines: - Always be helpful, friendly, and concise - Provide accurate information based on the documentation - When you don't know something, say so clearly - Keep responses conversational for voice interaction - Use the docsquery tool whenever users ask specific questions about features, setup, or implementation - Summarize complex information in an easy-to-understand way - Ask clarifying questions if the user's request is unclear - Provide step-by-step guidance when explaining processes""" # Create the documentation assistant assistant = client.assistants.create( name="Docs agent", model={ "provider": "anthropic", "model": "claude-3-5-sonnet-20241022", # Valid Claude model "maxTokens": 400, "messages": [ { "role": "system", "content": system_prompt } ], "toolIds": [YOUR_TOOL_ID_FROM_STEP_2] # Replace with actual tool ID }, // Configure voice settings voice={ "provider": "vapi", "version": 2, "voiceId": "Elliot" }, // Configure transcription transcriber={ "provider": "deepgram", "model": "nova-2", "language": "en" }, // Set greeting message first_message="Hey I'm Harry, a support agent. How can I help you today? You can ask me questions about Vapi, how to get started or our documentation.", end_call_message="Goodbye.", background_sound="off", // Enable call analysis for continuous improvement analysis_plan={ "summaryPlan": { "enabled": True, "prompt": "Summarize this documentation support call, focusing on the user's questions and how well they were answered." }, "successEvaluationPlan": { "enabled": True, "prompt": "Evaluate if this documentation support call was successful. Did the user get helpful answers to their questions?", "rubric": "NumericScale" } } ) print(f"Assistant created with ID: {assistant.id}") ``` ```bash curl -X POST https://api.vapi.ai/tool \ -H "Authorization: Bearer YOUR_VAPI_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "type": "apiRequest", "name": "docsquery", "function": { "name": "docsquery", "parameters": { "type": "object", "properties": { "query": { "type": "string", "description": "The search query to find relevant documentation" } }, "required": ["query"] } }, "url": "https://api.cloud.llamaindex.ai/api/v1/pipelines/YOUR_PIPELINE_ID/retrieve", "method": "POST", "headers": { "type": "object", "properties": { "Content-Type": { "type": "string", "value": "application/json" }, "Authorization": { "type": "string", "value": "Bearer YOUR_LLAMACLOUD_API_KEY" } } }, "body": { "type": "object", "properties": { "query": { "type": "string", "value": "{{query}}" } } } }' ``` Replace `YOUR_PIPELINE_ID` with your LlamaCloud pipeline ID and `YOUR_LLAMACLOUD_API_KEY` with your API key. Save the tool ID from the response for the next step. Create an assistant with the RAG tool attached. 1. Navigate to **Assistants** in your [Vapi Dashboard](https://dashboard.vapi.ai/) 2. Click **Create Assistant** 3. Configure the assistant: * **Name**: `Docs agent` * **Model**: Claude Sonnet 4 (Anthropic) * **Voice**: Elliot (Vapi) * **First Message**: `Hey I'm Harry, a support agent. How can I help you today? You can ask me questions about Vapi, how to get started or our documentation.` * **System Prompt**: Use a helpful documentation assistant prompt with guidelines for using the docsquery tool 4. Add the `docsquery` tool in the Tools section 5. Configure analysis plan for call monitoring 6. Publish the assistant ```typescript import { VapiClient } from "@vapi-ai/server-sdk"; // Initialize Vapi server SDK const vapi = new VapiClient({ token: "YOUR_VAPI_API_KEY" }); // Define system prompt for documentation assistant const systemPrompt = `You are a helpful documentation assistant. Use the docsquery tool to find relevant information when users ask questions about the documentation. Guidelines: - Always be helpful, friendly, and concise - Provide accurate information based on the documentation - When you don't know something, say so clearly - Keep responses conversational for voice interaction - Use the docsquery tool whenever users ask specific questions about features, setup, or implementation - Summarize complex information in an easy-to-understand way - Ask clarifying questions if the user's request is unclear - Provide step-by-step guidance when explaining processes`; // Create the documentation assistant const assistant = await vapi.assistants.create({ name: "Docs agent", model: { provider: "anthropic", model: "claude-3-5-sonnet-20241022", # Valid Claude model maxTokens: 400, messages: [ { role: "system", content: systemPrompt } ], toolIds: [YOUR_TOOL_ID_FROM_STEP_2] // Replace with actual tool ID }, // Configure voice settings voice: { provider: "vapi", version: 2, voiceId: "Elliot" }, // Configure transcription transcriber: { provider: "deepgram", model: "nova-2", language: "en" }, // Set greeting message firstMessage: "Hey I'm Harry, a support agent. How can I help you today? You can ask me questions about Vapi, how to get started or our documentation.", endCallMessage: "Goodbye.", backgroundSound: "off", // Enable call analysis for continuous improvement analysisPlan: { summaryPlan: { enabled: true, prompt: "Summarize this documentation support call, focusing on the user's questions and how well they were answered." }, successEvaluationPlan: { enabled: true, prompt: "Evaluate if this documentation support call was successful. Did the user get helpful answers to their questions?", rubric: "NumericScale" } } }); console.log(`Assistant created with ID: ${assistant.id}`); ``` ```python from vapi import Vapi # Initialize Vapi server SDK client = Vapi(token="YOUR_VAPI_API_KEY") # Define system prompt for documentation assistant system_prompt = """You are a helpful documentation assistant. Use the docsquery tool to find relevant information when users ask questions about the documentation. Guidelines: - Always be helpful, friendly, and concise - Provide accurate information based on the documentation - When you don't know something, say so clearly - Keep responses conversational for voice interaction - Use the docsquery tool whenever users ask specific questions about features, setup, or implementation - Summarize complex information in an easy-to-understand way - Ask clarifying questions if the user's request is unclear - Provide step-by-step guidance when explaining processes""" # Create the documentation assistant assistant = client.assistants.create( name="Docs agent", model={ "provider": "anthropic", "model": "claude-3-5-sonnet-20241022", # Valid Claude model "maxTokens": 400, "messages": [ { "role": "system", "content": system_prompt } ], "toolIds": [YOUR_TOOL_ID_FROM_STEP_2] # Replace with actual tool ID }, // Configure voice settings voice={ "provider": "vapi", "version": 2, "voiceId": "Elliot" }, // Configure transcription transcriber={ "provider": "deepgram", "model": "nova-2", "language": "en" }, // Set greeting message first_message="Hey I'm Harry, a support agent. How can I help you today? You can ask me questions about Vapi, how to get started or our documentation.", end_call_message="Goodbye.", background_sound="off", // Enable call analysis for continuous improvement analysis_plan={ "summaryPlan": { "enabled": True, "prompt": "Summarize this documentation support call, focusing on the user's questions and how well they were answered." }, "successEvaluationPlan": { "enabled": True, "prompt": "Evaluate if this documentation support call was successful. Did the user get helpful answers to their questions?", "rubric": "NumericScale" } } ) print(f"Assistant created with ID: {assistant.id}") ``` ```bash curl -X POST https://api.vapi.ai/tool \ -H "Authorization: Bearer YOUR_VAPI_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "type": "apiRequest", "name": "docsquery", "function": { "name": "docsquery", "parameters": { "type": "object", "properties": { "query": { "type": "string", "description": "The search query to find relevant documentation" } }, "required": ["query"] } }, "url": "https://api.cloud.llamaindex.ai/api/v1/pipelines/YOUR_PIPELINE_ID/retrieve", "method": "POST", "headers": { "type": "object", "properties": { "Content-Type": { "type": "string", "value": "application/json" }, "Authorization": { "type": "string", "value": "Bearer YOUR_LLAMACLOUD_API_KEY" } } }, "body": { "type": "object", "properties": { "query": { "type": "string", "value": "{{query}}" } } } }' ``` Replace `YOUR_PIPELINE_ID` with your LlamaCloud pipeline ID and `YOUR_LLAMACLOUD_API_KEY` with your API key. Save the tool ID from the response for the next step. Customize your assistant's behavior and responses after creation. 1. Navigate to your assistant in the [Vapi Dashboard](https://dashboard.vapi.ai/) 2. Edit any properties like system prompt, first message, or voice settings 3. Changes apply immediately - no republishing needed ```typescript import { VapiClient } from "@vapi-ai/server-sdk"; const vapi = new VapiClient({ token: "YOUR_VAPI_API_KEY" }); // Update the assistant's first message const updatedAssistant = await vapi.assistants.update("YOUR_ASSISTANT_ID", { firstMessage: "Hello! I'm your documentation assistant. What would you like to know about our platform?", // Update system prompt for better responses model: { provider: "anthropic", model: "claude-3-5-sonnet-20241022", messages: [ { role: "system", content: "Enhanced system prompt with more specific guidelines for documentation assistance" } ] } }); console.log("Assistant updated successfully"); ``` ```python import requests # Update assistant properties url = f"https://api.vapi.ai/assistant/{YOUR_ASSISTANT_ID}" headers = { "Authorization": f"Bearer {YOUR_VAPI_API_KEY}", "Content-Type": "application/json" } # Update first message and system prompt data = { "firstMessage": "Hello! I'\''m your documentation assistant. What would you like to know about our platform?", "model": { "provider": "anthropic", "model": "claude-3-5-sonnet-20241022", "messages": [ { "role": "system", "content": "Enhanced system prompt with more specific guidelines for documentation assistance" } ] } } response = requests.patch(url, headers=headers, json=data) print("Assistant updated successfully") ``` ```bash curl -X PATCH https://api.vapi.ai/assistant/YOUR_ASSISTANT_ID \ -H "Authorization: Bearer YOUR_VAPI_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "firstMessage": "Hello! I'\''m your documentation assistant. What would you like to know about our platform?", "model": { "provider": "anthropic", "model": "claude-3-5-sonnet-20241022", "messages": [ { "role": "system", "content": "Enhanced system prompt with more specific guidelines for documentation assistance" } ] } }' ``` SDK changes apply immediately. Unlike Dashboard publishing, programmatic updates take effect right away. Create test scenarios to validate your documentation assistant's responses. 1. Navigate to **Test** > **Voice Test Suites** in your dashboard 2. Click **Create Test Suite** 3. Add test scenarios with expected behaviors 4. Run tests to validate assistant performance ```typescript import { VapiClient } from "@vapi-ai/server-sdk"; const vapi = new VapiClient({ token: "YOUR_VAPI_API_KEY" }); // Create test suite for documentation assistant const testSuite = await vapi.testSuites.create({ name: "Documentation Assistant Tests", assistantId: "YOUR_ASSISTANT_ID", testCases: [ { name: "Basic greeting test", scenario: "User says hello", expectedBehavior: "Assistant responds with greeting and asks how to help" }, { name: "Documentation query test", scenario: "User asks about API endpoints", expectedBehavior: "Assistant uses docsquery tool and provides relevant information" }, { name: "Unknown topic test", scenario: "User asks about unrelated topic", expectedBehavior: "Assistant politely redirects to documentation topics" } ] }); console.log(`Test suite created with ID: ${testSuite.id}`); console.log("Next: Go to Dashboard to run the test suite"); ``` ```python import requests # Create test suite for documentation assistant url = "https://api.vapi.ai/test-suite" headers = { "Authorization": f"Bearer {YOUR_VAPI_API_KEY}", "Content-Type": "application/json" } data = { "name": "Documentation Assistant Tests", "assistantId": "YOUR_ASSISTANT_ID", "testCases": [ { "name": "Basic greeting test", "scenario": "User says hello", "expectedBehavior": "Assistant responds with greeting and asks how to help" }, { "name": "Documentation query test", "scenario": "User asks about API endpoints", "expectedBehavior": "Assistant uses docsquery tool and provides relevant information" }, { "name": "Unknown topic test", "scenario": "User asks about unrelated topic", "expectedBehavior": "Assistant politely redirects to documentation topics" } ] } response = requests.post(url, headers=headers, json=data) test_suite = response.json() print(f"Test suite created with ID: {test_suite['id']}") print("Next: Go to Dashboard to run the test suite") ``` ```bash curl -X POST https://api.vapi.ai/test-suite \ -H "Authorization: Bearer YOUR_VAPI_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "name": "Documentation Assistant Tests", "assistantId": "YOUR_ASSISTANT_ID", "testCases": [ { "name": "Basic greeting test", "scenario": "User says hello", "expectedBehavior": "Assistant responds with greeting and asks how to help" }, { "name": "Documentation query test", "scenario": "User asks about API endpoints", "expectedBehavior": "Assistant uses docsquery tool and provides relevant information" }, { "name": "Unknown topic test", "scenario": "User asks about unrelated topic", "expectedBehavior": "Assistant politely redirects to documentation topics" } ] }' ``` Test suites can only be executed through the Dashboard. Navigate to **Test** > **Voice Test Suites** to run your created tests. Use the Vapi Web SDK to create a voice widget for your documentation assistant. ```bash title="npm" npm install @vapi-ai/web ``` ```bash title="yarn" yarn add @vapi-ai/web ``` ```bash title="pnpm" pnpm add @vapi-ai/web ``` ```bash title="bun" bun add @vapi-ai/web ``` Replace `YOUR_PUBLIC_API_KEY` and `YOUR_ASSISTANT_ID` with your actual values: ```typescript import { useState, useEffect } from 'react'; import Vapi from '@vapi-ai/web'; export default function VoiceWidget() { const [vapi, setVapi] = useState(null); const [isConnected, setIsConnected] = useState(false); const [transcript, setTranscript] = useState([]); useEffect(() => { // Initialize Vapi Web SDK for client-side voice interactions const vapiInstance = new Vapi('YOUR_PUBLIC_API_KEY'); setVapi(vapiInstance); // Handle call lifecycle events vapiInstance.on('call-start', () => setIsConnected(true)); vapiInstance.on('call-end', () => setIsConnected(false)); // Handle real-time conversation messages vapiInstance.on('message', (msg) => { if (msg.type === 'transcript') { setTranscript(prev => [...prev, { role: msg.role, text: msg.transcript }]); } }); // Cleanup on component unmount return () => vapiInstance?.stop(); }, []); // Start voice conversation with documentation assistant const startCall = () => vapi?.start('YOUR_ASSISTANT_ID'); const endCall = () => vapi?.stop(); return (
{!isConnected ? ( ) : (
{transcript.map((msg, i) => (
{msg.text}
))}
)}
); } ``` For a complete implementation with waveform visualization, real-time transcripts, and responsive design, check out our [voice widget component](https://github.com/VapiAI/docs/blob/7879817ad2789d5929842cecff4ef3f4ec82acae/fern/widget/voice-widget.tsx) on GitHub. Vapi automatically analyzes every call. The assistant above includes an [`analysisPlan`](/api-reference/assistants/create#request.body.analysisPlan) with summary and success evaluation configured. 1. Navigate to **Logs** > **Calls** in your dashboard 2. Click on any completed call to view analysis 3. Review summary and success evaluation 4. Use insights to improve your assistant's prompts and responses ```typescript import { VapiClient } from "@vapi-ai/server-sdk"; const vapi = new VapiClient({ token: "YOUR_VAPI_API_KEY" }); // Retrieve call analysis for continuous improvement async function getCallAnalysis(callId: string) { try { const call = await vapi.calls.get(callId); if (call.analysis) { console.log("Call Summary:", call.analysis.summary); console.log("Success Score:", call.analysis.successEvaluation); // Use analysis data to improve prompts return { summary: call.analysis.summary, successScore: call.analysis.successEvaluation, improvements: extractImprovements(call.analysis) }; } } catch (error) { console.error("Error fetching call analysis:", error); } return call; } // Extract improvement suggestions from analysis function extractImprovements(analysis: any) { // Analyze patterns to suggest prompt improvements return { promptSuggestions: "Based on call analysis...", commonQueries: "Users frequently ask about...", successFactors: "Successful calls typically..." }; } // Get analysis for a specific call const analysis = await getCallAnalysis("CALL_ID"); ``` ```python import requests def get_call_analysis(call_id): """Retrieve and analyze call data for continuous improvement""" url = f"https://api.vapi.ai/call/{call_id}" headers = {"Authorization": f"Bearer {YOUR_VAPI_API_KEY}"} try: response = requests.get(url, headers=headers) call_data = response.json() if 'analysis' in call_data: print("Call Summary:", call_data['analysis']['summary']) print("Success Score:", call_data['analysis']['successEvaluation']) # Extract insights for improvement return { 'summary': call_data['analysis']['summary'], 'success_score': call_data['analysis']['successEvaluation'], 'improvements': extract_improvements(call_data['analysis']) } except Exception as error: print(f"Error fetching call analysis: {error}") return call_data def extract_improvements(analysis): """Extract improvement suggestions from analysis data""" return { 'prompt_suggestions': "Based on call analysis...", 'common_queries': "Users frequently ask about...", 'success_factors': "Successful calls typically..." } # Get analysis for a specific call analysis = get_call_analysis("CALL_ID") ``` Retrieve analysis results using the Get Call API: ```bash curl https://api.vapi.ai/call/CALL_ID \ -H "Authorization: Bearer YOUR_VAPI_API_KEY" ``` **Iterative improvements:** * Review analysis summaries to identify common user questions * Use structured data to track conversation patterns * Monitor success evaluations to optimize assistant performance * Update your system prompt based on recurring issues * Refine RAG retrieval based on query success patterns Learn advanced prompting techniques to optimize your documentation agent's responses and behavior.