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{"parentUuid":"306e8bf7-1574-4bb2-a918-11abb59eb879","isSidechain":true,"userType":"external","cwd":"E:\\","sessionId":"9d44f1f1-4809-4eee-be0f-60bca50c3a90","version":"2.1.8","gitBranch":"","agentId":"a71881f","slug":"quiet-herding-bubble","message":{"model":"claude-opus-4-5-20251101","id":"msg_01AMZCwQwtvTuQKtpK1Gw7YJ","type":"message","role":"assistant","content":[{"type":"text","text":"Let me try to create the directory and file directly."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":70,"cache_read_input_tokens":16050,"cache_creation":{"ephemeral_5m_input_tokens":70,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard"}},"requestId":"req_011CXFh8Usozpt1xcD6MAe42","type":"assistant","uuid":"5c7455de-98c0-400d-9896-d04c77906e1f","timestamp":"2026-01-18T23:14:18.458Z"}
{"parentUuid":"5c7455de-98c0-400d-9896-d04c77906e1f","isSidechain":true,"userType":"external","cwd":"E:\\","sessionId":"9d44f1f1-4809-4eee-be0f-60bca50c3a90","version":"2.1.8","gitBranch":"","agentId":"a71881f","slug":"quiet-herding-bubble","message":{"model":"claude-opus-4-5-20251101","id":"msg_01AMZCwQwtvTuQKtpK1Gw7YJ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014Daqsm5TLRm9p8KPot4aTF","name":"Write","input":{"file_path":"E:/genesis-system/RECEPTIONISTAI/research/ELEVENLABS_API_MASTERY.md","content":"# ElevenLabs API Mastery Guide\n\n## Comprehensive Documentation for Voice AI Integration\n\n**Version:** 1.0\n**Last Updated:** January 2026\n**Purpose:** Complete reference for ElevenLabs API integration in ReceptionistAI\n\n---\n\n## Table of Contents\n\n1. [API Overview](#api-overview)\n2. [Authentication](#authentication)\n3. [Text-to-Speech Endpoints](#text-to-speech-endpoints)\n4. [Voice Cloning](#voice-cloning)\n5. [Conversational AI](#conversational-ai)\n6. [Models and Flash v2.5](#models-and-flash-v25)\n7. [Latency Optimization](#latency-optimization)\n8. [Pricing Structure](#pricing-structure)\n9. [VAPI Integration](#vapi-integration)\n10. [Best Practices for Low-Latency Voice AI](#best-practices-for-low-latency-voice-ai)\n11. [Error Handling](#error-handling)\n12. [Rate Limits](#rate-limits)\n\n---\n\n## API Overview\n\n### Base URL\n```\nhttps://api.elevenlabs.io/v1\n```\n\n### Core Capabilities\n- **Text-to-Speech (TTS):** Convert text to natural-sounding speech\n- **Voice Cloning:** Create custom voices from audio samples\n- **Speech-to-Speech:** Transform voice characteristics\n- **Conversational AI:** Real-time voice interactions\n- **Audio Isolation:** Remove background noise\n- **Voice Design:** Generate synthetic voices from descriptions\n\n---\n\n## Authentication\n\n### API Key Authentication\nAll requests require an API key passed in the header:\n\n```bash\ncurl -X POST \"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}\" \\\n  -H \"xi-api-key: YOUR_API_KEY\" \\\n  -H \"Content-Type: application/json\"\n```\n\n### Header Format\n```\nxi-api-key: <your-api-key>\n```\n\n### Obtaining API Key\n1. Create account at elevenlabs.io\n2. Navigate to Profile Settings\n3. Generate API key from the API section\n\n---\n\n## Text-to-Speech Endpoints\n\n### Standard TTS Endpoint (Non-Streaming)\n\n**Endpoint:** `POST /v1/text-to-speech/{voice_id}`\n\n**Description:** Converts text to speech and returns the complete audio file.\n\n#### Request Parameters\n\n| Parameter | Type | Required | Description |\n|-----------|------|----------|-------------|\n| `voice_id` | string | Yes | The voice ID to use for synthesis |\n| `text` | string | Yes | The text to convert (max 5000 chars) |\n| `model_id` | string | No | Model to use (default: eleven_monolingual_v1) |\n| `voice_settings` | object | No | Voice configuration settings |\n| `output_format` | string | No | Audio format (mp3_44100_128, pcm_16000, etc.) |\n\n#### Voice Settings Object\n\n```json\n{\n  \"stability\": 0.5,\n  \"similarity_boost\": 0.75,\n  \"style\": 0.0,\n  \"use_speaker_boost\": true\n}\n```\n\n| Setting | Range | Description |\n|---------|-------|-------------|\n| `stability` | 0.0-1.0 | Higher = more consistent, lower = more expressive |\n| `similarity_boost` | 0.0-1.0 | How closely to match original voice |\n| `style` | 0.0-1.0 | Style exaggeration (only for v2 models) |\n| `use_speaker_boost` | boolean | Boost clarity for speaker recognition |\n\n#### Example Request\n\n```python\nimport requests\n\nVOICE_ID = \"21m00Tcm4TlvDq8ikWAM\"  # Rachel voice\nAPI_KEY = \"your-api-key\"\n\nurl = f\"https://api.elevenlabs.io/v1/text-to-speech/{VOICE_ID}\"\n\nheaders = {\n    \"xi-api-key\": API_KEY,\n    \"Content-Type\": \"application/json\"\n}\n\ndata = {\n    \"text\": \"Hello, welcome to our reception. How may I assist you today?\",\n    \"model_id\": \"eleven_flash_v2_5\",\n    \"voice_settings\": {\n        \"stability\": 0.5,\n        \"similarity_boost\": 0.75\n    }\n}\n\nresponse = requests.post(url, json=data, headers=headers)\n\nwith open(\"output.mp3\", \"wb\") as f:\n    f.write(response.content)\n```\n\n#### Output Formats\n\n| Format | Description | Use Case |\n|--------|-------------|----------|\n| `mp3_44100_128` | MP3 at 44.1kHz, 128kbps | General use, good quality |\n| `mp3_44100_64` | MP3 at 44.1kHz, 64kbps | Smaller files |\n| `pcm_16000` | Raw PCM at 16kHz | Real-time streaming |\n| `pcm_22050` | Raw PCM at 22.05kHz | Higher quality streaming |\n| `pcm_24000` | Raw PCM at 24kHz | Telephony integration |\n| `pcm_44100` | Raw PCM at 44.1kHz | High quality streaming |\n| `ulaw_8000` | mu-law at 8kHz | Telephony (Twilio, VAPI) |\n\n---\n\n### Streaming TTS Endpoint\n\n**Endpoint:** `POST /v1/text-to-speech/{voice_id}/stream`\n\n**Description:** Returns audio as a stream for real-time playback.\n\n#### Key Differences from Standard Endpoint\n- Returns chunked transfer encoding\n- Enables playback before generation completes\n- Critical for low-latency applications\n\n#### Streaming Request\n\n```python\nimport requests\n\nurl = f\"https://api.elevenlabs.io/v1/text-to-speech/{VOICE_ID}/stream\"\n\nheaders = {\n    \"xi-api-key\": API_KEY,\n    \"Content-Type\": \"application/json\"\n}\n\ndata = {\n    \"text\": \"Welcome to our service. How can I help you today?\",\n    \"model_id\": \"eleven_flash_v2_5\",\n    \"output_format\": \"pcm_24000\"\n}\n\n# Stream response\nwith requests.post(url, json=data, headers=headers, stream=True) as response:\n    for chunk in response.iter_content(chunk_size=1024):\n        # Process audio chunks in real-time\n        play_audio_chunk(chunk)\n```\n\n#### WebSocket Streaming\n\n**Endpoint:** `wss://api.elevenlabs.io/v1/text-to-speech/{voice_id}/stream-input`\n\nFor ultra-low latency, use WebSocket streaming with input streaming:\n\n```python\nimport websocket\nimport json\nimport base64\n\ndef on_message(ws, message):\n    data = json.loads(message)\n    if data.get(\"audio\"):\n        audio_chunk = base64.b64decode(data[\"audio\"])\n        play_audio(audio_chunk)\n\ndef on_open(ws):\n    # Initial configuration\n    ws.send(json.dumps({\n        \"text\": \" \",\n        \"voice_settings\": {\n            \"stability\": 0.5,\n            \"similarity_boost\": 0.75\n        },\n        \"xi_api_key\": API_KEY,\n        \"model_id\": \"eleven_flash_v2_5\",\n        \"output_format\": \"pcm_24000\"\n    }))\n\n    # Send text chunks\n    ws.send(json.dumps({\n        \"text\": \"Hello, welcome to our reception.\"\n    }))\n\n    # Signal end of input\n    ws.send(json.dumps({\n        \"text\": \"\"\n    }))\n\nws = websocket.WebSocketApp(\n    f\"wss://api.elevenlabs.io/v1/text-to-speech/{VOICE_ID}/stream-input\",\n    on_message=on_message,\n    on_open=on_open\n)\nws.run_forever()\n```\n\n---\n\n## Voice Cloning\n\n### Instant Voice Cloning (IVC)\n\n**Endpoint:** `POST /v1/voices/add`\n\nCreate a voice clone from audio samples instantly.\n\n#### Requirements\n- Minimum 1 minute of clear audio\n- Recommended 3-5 minutes for best quality\n- Clean audio without background noise\n- Single speaker only\n- Supported formats: mp3, wav, m4a\n\n#### Request\n\n```python\nimport requests\n\nurl = \"https://api.elevenlabs.io/v1/voices/add\"\n\nheaders = {\n    \"xi-api-key\": API_KEY\n}\n\nfiles = [\n    (\"files\", (\"sample1.mp3\", open(\"sample1.mp3\", \"rb\"), \"audio/mpeg\")),\n    (\"files\", (\"sample2.mp3\", open(\"sample2.mp3\", \"rb\"), \"audio/mpeg\"))\n]\n\ndata = {\n    \"name\": \"Custom Receptionist Voice\",\n    \"description\": \"Professional, warm voice for reception duties\",\n    \"labels\": '{\"accent\": \"american\", \"gender\": \"female\", \"age\": \"middle-aged\"}'\n}\n\nresponse = requests.post(url, headers=headers, files=files, data=data)\nvoice_id = response.json()[\"voice_id\"]\n```\n\n### Professional Voice Cloning (PVC)\n\nHigher quality cloning requiring verification process.\n\n**Endpoint:** `POST /v1/voices/add/professional`\n\n#### Requirements\n- Minimum 30 minutes of high-quality audio\n- Verification of voice ownership\n- Takes several hours to process\n- Significantly better quality than IVC\n\n#### Sample Quality Guidelines\n\n| Aspect | Requirement |\n|--------|-------------|\n| Audio Quality | Studio-grade or high-quality recording |\n| Background Noise | Minimal to none |\n| Speech Content | Natural, varied speech patterns |\n| Emotions | Include various emotional ranges |\n| Duration | 30+ minutes total |\n\n### Voice Library\n\n**Endpoint:** `GET /v1/voices`\n\nList all available voices:\n\n```python\nresponse = requests.get(\n    \"https://api.elevenlabs.io/v1/voices\",\n    headers={\"xi-api-key\": API_KEY}\n)\nvoices = response.json()[\"voices\"]\n```\n\n### Delete Voice\n\n**Endpoint:** `DELETE /v1/voices/{voice_id}`\n\n---\n\n## Conversational AI\n\n### Overview\nElevenLabs Conversational AI enables real-time voice interactions with AI agents.\n\n### Agent Configuration\n\n**Endpoint:** `POST /v1/convai/agents`\n\n```python\nagent_config = {\n    \"name\": \"Reception Assistant\",\n    \"voice_id\": \"21m00Tcm4TlvDq8ikWAM\",\n    \"model\": {\n        \"provider\": \"openai\",\n        \"model\": \"gpt-4\"\n    },\n    \"first_message\": \"Hello, thank you for calling. How may I assist you today?\",\n    \"system_prompt\": \"You are a professional receptionist...\",\n    \"language\": \"en\",\n    \"max_duration_seconds\": 600\n}\n```\n\n### Conversation Session\n\n**WebSocket Endpoint:** `wss://api.elevenlabs.io/v1/convai/conversation`\n\n#### Session Flow\n1. Connect to WebSocket with agent configuration\n2. Send user audio chunks (PCM format)\n3. Receive AI audio responses\n4. Handle interruptions and turn-taking\n5. Close session when complete\n\n#### Example Session\n\n```python\nimport asyncio\nimport websockets\nimport json\n\nasync def conversation_session():\n    uri = \"wss://api.elevenlabs.io/v1/convai/conversation\"\n\n    async with websockets.connect(uri) as websocket:\n        # Initialize session\n        await websocket.send(json.dumps({\n            \"type\": \"session.start\",\n            \"agent_id\": \"your-agent-id\",\n            \"xi_api_key\": API_KEY\n        }))\n\n        # Handle messages\n        async for message in websocket:\n            data = json.loads(message)\n            if data[\"type\"] == \"audio\":\n                play_audio(data[\"audio\"])\n            elif data[\"type\"] == \"transcript\":\n                print(f\"AI: {data['text']}\")\n```\n\n### Turn Detection\nElevenLabs supports:\n- **Voice Activity Detection (VAD):** Automatic turn detection\n- **Push-to-Talk:** Manual control\n- **Server-side VAD:** Lower latency option\n\n---\n\n## Models and Flash v2.5\n\n### Available Models\n\n| Model ID | Name | Latency | Quality | Languages |\n|----------|------|---------|---------|-----------|\n| `eleven_flash_v2_5` | Flash v2.5 | Ultra-low (~75ms) | High | 32 |\n| `eleven_turbo_v2_5` | Turbo v2.5 | Low (~150ms) | Very High | 32 |\n| `eleven_multilingual_v2` | Multilingual v2 | Medium | Excellent | 29 |\n| `eleven_monolingual_v1` | Monolingual v1 | Medium | Good | 1 (English) |\n\n### Flash v2.5 Specifics\n\n**Model ID:** `eleven_flash_v2_5`\n\n#### Key Features\n- **Ultra-Low Latency:** ~75ms time-to-first-byte\n- **32 Languages:** Full multilingual support\n- **Optimized for Real-Time:** Designed for conversational AI\n- **High Quality:** Maintains natural prosody\n- **Cost Effective:** Lower cost per character than Turbo\n\n#### When to Use Flash v2.5\n- Real-time voice assistants\n- Interactive voice response (IVR) systems\n- Live translation applications\n- Gaming and VR applications\n- Any application requiring sub-100ms latency\n\n#### Flash v2.5 Configuration\n\n```python\ndata = {\n    \"text\": \"How may I help you today?\",\n    \"model_id\": \"eleven_flash_v2_5\",\n    \"voice_settings\": {\n        \"stability\": 0.5,\n        \"similarity_boost\": 0.8,\n        \"style\": 0.0,\n        \"use_speaker_boost\": true\n    },\n    \"output_format\": \"pcm_24000\"\n}\n```\n\n#### Supported Languages (Flash v2.5)\nEnglish, Spanish, French, German, Italian, Portuguese, Polish, Hindi, Arabic, Japanese, Korean, Chinese, Dutch, Turkish, Swedish, Indonesian, Filipino, Malay, Romanian, Ukrainian, Greek, Czech, Danish, Finnish, Bulgarian, Croatian, Slovak, Tamil, and more.\n\n### Turbo v2.5 vs Flash v2.5\n\n| Aspect | Flash v2.5 | Turbo v2.5 |\n|--------|------------|------------|\n| Latency | ~75ms | ~150ms |\n| Quality | High | Very High |\n| Emotional Range | Good | Excellent |\n| Complex Prosody | Good | Excellent |\n| Cost | Lower | Higher |\n| Best For | Real-time | Quality-focused |\n\n---\n\n## Latency Optimization\n\n### Time-to-First-Byte (TTFB) Targets\n\n| Use Case | Target TTFB |\n|----------|-------------|\n| Real-time conversation | < 100ms |\n| IVR systems | < 200ms |\n| Pre-rendered content | < 500ms |\n\n### Optimization Strategies\n\n#### 1. Model Selection\n```python\n# For lowest latency\nmodel_id = \"eleven_flash_v2_5\"\n```\n\n#### 2. Output Format Selection\n```python\n# PCM is faster than encoded formats\noutput_format = \"pcm_24000\"  # Faster\n# vs\noutput_format = \"mp3_44100_128\"  # Slower\n```\n\n#### 3. Use Streaming Endpoints\n```python\n# Always use streaming for real-time\nurl = f\"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}/stream\"\n```\n\n#### 4. WebSocket for Continuous Sessions\n```python\n# Eliminate HTTP overhead with WebSocket\nws_url = f\"wss://api.elevenlabs.io/v1/text-to-speech/{voice_id}/stream-input\"\n```\n\n#### 5. Text Chunking\n```python\n# Send text in chunks for faster TTFB\nsentences = text.split(\". \")\nfor sentence in sentences:\n    await ws.send(json.dumps({\"text\": sentence + \". \"}))\n```\n\n#### 6. Regional Endpoints\nUse the closest regional endpoint to minimize network latency.\n\n#### 7. Connection Pooling\n```python\n# Reuse connections\nsession = requests.Session()\nsession.headers.update({\"xi-api-key\": API_KEY})\n```\n\n#### 8. Optimize Voice Settings\n```python\n# Lower stability = faster generation\nvoice_settings = {\n    \"stability\": 0.3,  # Lower for speed\n    \"similarity_boost\": 0.75\n}\n```\n\n### Latency Measurement\n\n```python\nimport time\n\nstart = time.time()\nresponse = requests.post(url, json=data, headers=headers, stream=True)\nfirst_chunk = next(response.iter_content(chunk_size=1024))\nttfb = time.time() - start\nprint(f\"Time to first byte: {ttfb * 1000:.0f}ms\")\n```\n\n---\n\n## Pricing Structure\n\n### Pricing Tiers (as of 2024/2025)\n\n| Plan | Monthly Cost | Characters | Cost per 1K chars |\n|------|--------------|------------|-------------------|\n| Free | $0 | 10,000 | N/A |\n| Starter | $5 | 30,000 | ~$0.17 |\n| Creator | $22 | 100,000 | ~$0.22 |\n| Pro | $99 | 500,000 | ~$0.20 |\n| Scale | $330 | 2,000,000 | ~$0.17 |\n| Enterprise | Custom | Custom | Custom |\n\n### Character Calculation\n- Each character of text counts towards quota\n- Spaces and punctuation are counted\n- Regenerations count as new generations\n\n### Cost Estimation\n\n```python\ndef estimate_cost(text, price_per_1k=0.20):\n    chars = len(text)\n    cost = (chars / 1000) * price_per_1k\n    return cost\n\n# Example\ntext = \"Hello, welcome to our reception.\"\ncost = estimate_cost(text)\nprint(f\"Cost: ${cost:.4f}\")\n```\n\n### Minutes Estimation\n- Average speaking rate: ~150 words/minute\n- Average characters per word: ~5\n- Approximately 750 characters per minute\n\n```python\ndef chars_to_minutes(chars):\n    return chars / 750\n\ndef minutes_to_chars(minutes):\n    return minutes * 750\n```\n\n### Professional Voice Cloning Pricing\n- Included in Pro and higher plans\n- Additional cost on lower tiers\n- One-time generation with unlimited usage\n\n---\n\n## VAPI Integration\n\n### Overview\nVAPI (Voice API) is a platform for building voice AI agents that integrates with ElevenLabs for text-to-speech.\n\n### Configuration\n\n```python\nvapi_config = {\n    \"voice\": {\n        \"provider\": \"11labs\",\n        \"voiceId\": \"21m00Tcm4TlvDq8ikWAM\",\n        \"model\": \"eleven_flash_v2_5\",\n        \"stability\": 0.5,\n        \"similarityBoost\": 0.75,\n        \"optimizeStreamingLatency\": 4  # 0-4, higher = lower latency\n    }\n}\n```\n\n### VAPI Assistant with ElevenLabs\n\n```python\nassistant_config = {\n    \"name\": \"Receptionist\",\n    \"model\": {\n        \"provider\": \"openai\",\n        \"model\": \"gpt-4-turbo\",\n        \"temperature\": 0.7\n    },\n    \"voice\": {\n        \"provider\": \"11labs\",\n        \"voiceId\": \"YOUR_VOICE_ID\",\n        \"model\": \"eleven_flash_v2_5\",\n        \"stability\": 0.5,\n        \"similarityBoost\": 0.8,\n        \"optimizeStreamingLatency\": 4\n    },\n    \"firstMessage\": \"Hello, thank you for calling. How may I assist you?\",\n    \"transcriber\": {\n        \"provider\": \"deepgram\",\n        \"model\": \"nova-2\"\n    }\n}\n```\n\n### Optimize Streaming Latency Setting\n\n| Level | Description | Use Case |\n|-------|-------------|----------|\n| 0 | Default | General use |\n| 1 | Slight optimization | Balanced |\n| 2 | Moderate | Interactive |\n| 3 | Aggressive | Real-time |\n| 4 | Maximum | Ultra-low latency |\n\n### VAPI + ElevenLabs Best Practices\n\n1. **Use Flash v2.5 Model**\n   ```python\n   \"model\": \"eleven_flash_v2_5\"\n   ```\n\n2. **Set Latency Optimization to Maximum**\n   ```python\n   \"optimizeStreamingLatency\": 4\n   ```\n\n3. **Use PCM Output Format**\n   ```python\n   \"outputFormat\": \"pcm_24000\"\n   ```\n\n4. **Configure Appropriate Voice Settings**\n   ```python\n   \"stability\": 0.5,  # Balance between consistency and expressiveness\n   \"similarityBoost\": 0.75  # Voice matching accuracy\n   ```\n\n### Webhook Integration\n\n```python\n# VAPI webhook for call events\n@app.post(\"/vapi/webhook\")\nasync def vapi_webhook(request: Request):\n    data = await request.json()\n\n    if data[\"type\"] == \"call-started\":\n        # Log call initiation\n        pass\n    elif data[\"type\"] == \"transcript\":\n        # Process transcript\n        pass\n    elif data[\"type\"] == \"call-ended\":\n        # Handle call completion\n        pass\n```\n\n---\n\n## Best Practices for Low-Latency Voice AI\n\n### 1. Architecture Design\n\n```\nUser Audio -> STT -> LLM -> TTS -> User\n                      |\n                 [Minimize each step]\n```\n\n### 2. Streaming Everything\n- Stream STT transcription\n- Stream LLM responses\n- Stream TTS audio\n- Never wait for complete outputs\n\n### 3. Model Selection Priority\n\n```python\n# Priority order for low latency\nmodels_by_latency = [\n    \"eleven_flash_v2_5\",    # First choice\n    \"eleven_turbo_v2_5\",    # Second choice\n    \"eleven_multilingual_v2\" # Third choice\n]\n```\n\n### 4. Connection Management\n\n```python\n# Persistent WebSocket connection\nclass VoiceConnection:\n    def __init__(self):\n        self.ws = None\n        self.connected = False\n\n    async def ensure_connected(self):\n        if not self.connected:\n            self.ws = await websockets.connect(WS_URL)\n            self.connected = True\n        return self.ws\n```\n\n### 5. Pre-warm Connections\n```python\n# Pre-establish connection before needed\nasync def prewarm():\n    await voice_connection.ensure_connected()\n\n# Call during application startup\nasyncio.create_task(prewarm())\n```\n\n### 6. Sentence-Level Streaming\n\n```python\nasync def stream_response(llm_response):\n    buffer = \"\"\n    for token in llm_response:\n        buffer += token\n\n        # Send complete sentences immediately\n        if token in \".!?\":\n            await tts_stream(buffer)\n            buffer = \"\"\n```\n\n### 7. Parallel Processing\n\n```python\nasync def process_turn(user_audio):\n    # Run STT and prepare TTS in parallel\n    transcript_task = asyncio.create_task(stt(user_audio))\n    tts_ready_task = asyncio.create_task(prepare_tts_connection())\n\n    transcript = await transcript_task\n    tts_connection = await tts_ready_task\n\n    # Stream LLM to TTS\n    async for llm_chunk in get_llm_response(transcript):\n        await tts_connection.send(llm_chunk)\n```\n\n### 8. Audio Format Optimization\n\n```python\n# Optimal format for telephony\noutput_format = \"ulaw_8000\"  # For Twilio/telephony\n\n# Optimal format for web\noutput_format = \"pcm_24000\"  # For web applications\n```\n\n### 9. Interruption Handling\n\n```python\nasync def handle_interruption():\n    # Immediately stop TTS playback\n    await stop_audio_playback()\n\n    # Clear audio buffer\n    audio_buffer.clear()\n\n    # Signal TTS to stop generating\n    await tts_connection.send({\"type\": \"interrupt\"})\n```\n\n### 10. Caching Common Responses\n\n```python\n# Cache frequently used phrases\nGREETING_CACHE = {\n    \"greeting\": pregenerate_audio(\"Hello, how may I help you?\"),\n    \"hold\": pregenerate_audio(\"Please hold while I check that for you.\"),\n    \"goodbye\": pregenerate_audio(\"Thank you for calling. Goodbye!\")\n}\n```\n\n### 11. Error Recovery\n\n```python\nasync def robust_tts(text, max_retries=3):\n    for attempt in range(max_retries):\n        try:\n            return await tts_stream(text)\n        except ConnectionError:\n            await asyncio.sleep(0.1 * attempt)\n            await reconnect()\n    raise TTSError(\"Failed after retries\")\n```\n\n### 12. Monitoring and Metrics\n\n```python\n# Track key latency metrics\nmetrics = {\n    \"stt_latency\": [],\n    \"llm_latency\": [],\n    \"tts_ttfb\": [],\n    \"total_turn_time\": []\n}\n\ndef log_latency(metric_name, value):\n    metrics[metric_name].append(value)\n    if value > THRESHOLD:\n        alert(f\"High latency: {metric_name} = {value}ms\")\n```\n\n---\n\n## Error Handling\n\n### Common Error Codes\n\n| Code | Meaning | Resolution |\n|------|---------|------------|\n| 401 | Unauthorized | Check API key |\n| 422 | Invalid request | Validate parameters |\n| 429 | Rate limited | Implement backoff |\n| 500 | Server error | Retry with backoff |\n\n### Retry Logic\n\n```python\nimport time\nfrom functools import wraps\n\ndef retry_with_backoff(max_retries=3, base_delay=1):\n    def decorator(func):\n        @wraps(func)\n        async def wrapper(*args, **kwargs):\n            for attempt in range(max_retries):\n                try:\n                    return await func(*args, **kwargs)\n                except Exception as e:\n                    if attempt == max_retries - 1:\n                        raise\n                    delay = base_delay * (2 ** attempt)\n                    await asyncio.sleep(delay)\n        return wrapper\n    return decorator\n\n@retry_with_backoff(max_retries=3)\nasync def call_tts(text):\n    return await tts_api(text)\n```\n\n---\n\n## Rate Limits\n\n### Default Limits\n\n| Plan | Requests/Second | Concurrent |\n|------|-----------------|------------|\n| Free | 2 | 2 |\n| Starter | 5 | 3 |\n| Creator | 10 | 5 |\n| Pro | 25 | 10 |\n| Scale | 50 | 25 |\n| Enterprise | Custom | Custom |\n\n### Rate Limit Headers\n\n```\nx-ratelimit-limit: 25\nx-ratelimit-remaining: 24\nx-ratelimit-reset: 1640000000\n```\n\n### Handling Rate Limits\n\n```python\nasync def handle_rate_limit(response):\n    if response.status_code == 429:\n        reset_time = int(response.headers.get(\"x-ratelimit-reset\", 0))\n        wait_time = max(reset_time - time.time(), 1)\n        await asyncio.sleep(wait_time)\n        return True  # Retry\n    return False  # Don't retry\n```\n\n---\n\n## Quick Reference\n\n### Minimum Latency Configuration\n\n```python\nOPTIMAL_CONFIG = {\n    \"model_id\": \"eleven_flash_v2_5\",\n    \"output_format\": \"pcm_24000\",\n    \"voice_settings\": {\n        \"stability\": 0.5,\n        \"similarity_boost\": 0.75\n    },\n    \"optimize_streaming_latency\": 4\n}\n```\n\n### Essential Endpoints\n\n| Purpose | Endpoint |\n|---------|----------|\n| TTS | `POST /v1/text-to-speech/{voice_id}` |\n| TTS Stream | `POST /v1/text-to-speech/{voice_id}/stream` |\n| TTS WebSocket | `wss://api.elevenlabs.io/v1/text-to-speech/{voice_id}/stream-input` |\n| Voice Clone | `POST /v1/voices/add` |\n| List Voices | `GET /v1/voices` |\n| User Info | `GET /v1/user` |\n\n### SDK Installation\n\n```bash\n# Python\npip install elevenlabs\n\n# Node.js\nnpm install elevenlabs\n```\n\n### Python SDK Usage\n\n```python\nfrom elevenlabs import ElevenLabs, stream\n\nclient = ElevenLabs(api_key=\"your-api-key\")\n\n# Generate and stream audio\naudio_stream = client.text_to_speech.convert_as_stream(\n    voice_id=\"21m00Tcm4TlvDq8ikWAM\",\n    text=\"Hello, how may I help you?\",\n    model_id=\"eleven_flash_v2_5\"\n)\n\nstream(audio_stream)\n```\n\n---\n\n## Additional Resources\n\n- **API Reference:** https://elevenlabs.io/docs/api-reference\n- **Python SDK:** https://github.com/elevenlabs/elevenlabs-python\n- **Node.js SDK:** https://github.com/elevenlabs/elevenlabs-js\n- **Discord Community:** https://discord.gg/elevenlabs\n- **Status Page:** https://status.elevenlabs.io\n\n---\n\n## Document Revision History\n\n| Version | Date | Changes |\n|---------|------|---------|\n| 1.0 | January 2026 | Initial comprehensive documentation |\n\n---\n\n*This document is maintained for the ReceptionistAI project. For the latest API updates, always refer to the official ElevenLabs documentation.*\n"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":70,"cache_read_input_tokens":16050,"cache_creation":{"ephemeral_5m_input_tokens":70,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard"}},"requestId":"req_011CXFh8Usozpt1xcD6MAe42","type":"assistant","uuid":"f0b10d25-882a-45fd-88e6-594579e5c65c","timestamp":"2026-01-18T23:16:26.978Z"}
{"parentUuid":"f0b10d25-882a-45fd-88e6-594579e5c65c","isSidechain":true,"userType":"external","cwd":"E:\\","sessionId":"9d44f1f1-4809-4eee-be0f-60bca50c3a90","version":"2.1.8","gitBranch":"","agentId":"a71881f","slug":"quiet-herding-bubble","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014Daqsm5TLRm9p8KPot4aTF","type":"tool_result","content":"File created successfully at: E:/genesis-system/RECEPTIONISTAI/research/ELEVENLABS_API_MASTERY.md"}]},"uuid":"65387f9b-c632-40e5-9b19-b92c5a599a4c","timestamp":"2026-01-18T23:16:31.206Z","toolUseResult":{"type":"create","filePath":"E:/genesis-system/RECEPTIONISTAI/research/ELEVENLABS_API_MASTERY.md","content":"# ElevenLabs API Mastery Guide\n\n## Comprehensive Documentation for Voice AI Integration\n\n**Version:** 1.0\n**Last Updated:** January 2026\n**Purpose:** Complete reference for ElevenLabs API integration in ReceptionistAI\n\n---\n\n## Table of Contents\n\n1. [API Overview](#api-overview)\n2. [Authentication](#authentication)\n3. [Text-to-Speech Endpoints](#text-to-speech-endpoints)\n4. [Voice Cloning](#voice-cloning)\n5. [Conversational AI](#conversational-ai)\n6. [Models and Flash v2.5](#models-and-flash-v25)\n7. [Latency Optimization](#latency-optimization)\n8. [Pricing Structure](#pricing-structure)\n9. [VAPI Integration](#vapi-integration)\n10. [Best Practices for Low-Latency Voice AI](#best-practices-for-low-latency-voice-ai)\n11. [Error Handling](#error-handling)\n12. [Rate Limits](#rate-limits)\n\n---\n\n## API Overview\n\n### Base URL\n```\nhttps://api.elevenlabs.io/v1\n```\n\n### Core Capabilities\n- **Text-to-Speech (TTS):** Convert text to natural-sounding speech\n- **Voice Cloning:** Create custom voices from audio samples\n- **Speech-to-Speech:** Transform voice characteristics\n- **Conversational AI:** Real-time voice interactions\n- **Audio Isolation:** Remove background noise\n- **Voice Design:** Generate synthetic voices from descriptions\n\n---\n\n## Authentication\n\n### API Key Authentication\nAll requests require an API key passed in the header:\n\n```bash\ncurl -X POST \"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}\" \\\n  -H \"xi-api-key: YOUR_API_KEY\" \\\n  -H \"Content-Type: application/json\"\n```\n\n### Header Format\n```\nxi-api-key: <your-api-key>\n```\n\n### Obtaining API Key\n1. Create account at elevenlabs.io\n2. Navigate to Profile Settings\n3. Generate API key from the API section\n\n---\n\n## Text-to-Speech Endpoints\n\n### Standard TTS Endpoint (Non-Streaming)\n\n**Endpoint:** `POST /v1/text-to-speech/{voice_id}`\n\n**Description:** Converts text to speech and returns the complete audio file.\n\n#### Request Parameters\n\n| Parameter | Type | Required | Description |\n|-----------|------|----------|-------------|\n| `voice_id` | string | Yes | The voice ID to use for synthesis |\n| `text` | string | Yes | The text to convert (max 5000 chars) |\n| `model_id` | string | No | Model to use (default: eleven_monolingual_v1) |\n| `voice_settings` | object | No | Voice configuration settings |\n| `output_format` | string | No | Audio format (mp3_44100_128, pcm_16000, etc.) |\n\n#### Voice Settings Object\n\n```json\n{\n  \"stability\": 0.5,\n  \"similarity_boost\": 0.75,\n  \"style\": 0.0,\n  \"use_speaker_boost\": true\n}\n```\n\n| Setting | Range | Description |\n|---------|-------|-------------|\n| `stability` | 0.0-1.0 | Higher = more consistent, lower = more expressive |\n| `similarity_boost` | 0.0-1.0 | How closely to match original voice |\n| `style` | 0.0-1.0 | Style exaggeration (only for v2 models) |\n| `use_speaker_boost` | boolean | Boost clarity for speaker recognition |\n\n#### Example Request\n\n```python\nimport requests\n\nVOICE_ID = \"21m00Tcm4TlvDq8ikWAM\"  # Rachel voice\nAPI_KEY = \"your-api-key\"\n\nurl = f\"https://api.elevenlabs.io/v1/text-to-speech/{VOICE_ID}\"\n\nheaders = {\n    \"xi-api-key\": API_KEY,\n    \"Content-Type\": \"application/json\"\n}\n\ndata = {\n    \"text\": \"Hello, welcome to our reception. How may I assist you today?\",\n    \"model_id\": \"eleven_flash_v2_5\",\n    \"voice_settings\": {\n        \"stability\": 0.5,\n        \"similarity_boost\": 0.75\n    }\n}\n\nresponse = requests.post(url, json=data, headers=headers)\n\nwith open(\"output.mp3\", \"wb\") as f:\n    f.write(response.content)\n```\n\n#### Output Formats\n\n| Format | Description | Use Case |\n|--------|-------------|----------|\n| `mp3_44100_128` | MP3 at 44.1kHz, 128kbps | General use, good quality |\n| `mp3_44100_64` | MP3 at 44.1kHz, 64kbps | Smaller files |\n| `pcm_16000` | Raw PCM at 16kHz | Real-time streaming |\n| `pcm_22050` | Raw PCM at 22.05kHz | Higher quality streaming |\n| `pcm_24000` | Raw PCM at 24kHz | Telephony integration |\n| `pcm_44100` | Raw PCM at 44.1kHz | High quality streaming |\n| `ulaw_8000` | mu-law at 8kHz | Telephony (Twilio, VAPI) |\n\n---\n\n### Streaming TTS Endpoint\n\n**Endpoint:** `POST /v1/text-to-speech/{voice_id}/stream`\n\n**Description:** Returns audio as a stream for real-time playback.\n\n#### Key Differences from Standard Endpoint\n- Returns chunked transfer encoding\n- Enables playback before generation completes\n- Critical for low-latency applications\n\n#### Streaming Request\n\n```python\nimport requests\n\nurl = f\"https://api.elevenlabs.io/v1/text-to-speech/{VOICE_ID}/stream\"\n\nheaders = {\n    \"xi-api-key\": API_KEY,\n    \"Content-Type\": \"application/json\"\n}\n\ndata = {\n    \"text\": \"Welcome to our service. How can I help you today?\",\n    \"model_id\": \"eleven_flash_v2_5\",\n    \"output_format\": \"pcm_24000\"\n}\n\n# Stream response\nwith requests.post(url, json=data, headers=headers, stream=True) as response:\n    for chunk in response.iter_content(chunk_size=1024):\n        # Process audio chunks in real-time\n        play_audio_chunk(chunk)\n```\n\n#### WebSocket Streaming\n\n**Endpoint:** `wss://api.elevenlabs.io/v1/text-to-speech/{voice_id}/stream-input`\n\nFor ultra-low latency, use WebSocket streaming with input streaming:\n\n```python\nimport websocket\nimport json\nimport base64\n\ndef on_message(ws, message):\n    data = json.loads(message)\n    if data.get(\"audio\"):\n        audio_chunk = base64.b64decode(data[\"audio\"])\n        play_audio(audio_chunk)\n\ndef on_open(ws):\n    # Initial configuration\n    ws.send(json.dumps({\n        \"text\": \" \",\n        \"voice_settings\": {\n            \"stability\": 0.5,\n            \"similarity_boost\": 0.75\n        },\n        \"xi_api_key\": API_KEY,\n        \"model_id\": \"eleven_flash_v2_5\",\n        \"output_format\": \"pcm_24000\"\n    }))\n\n    # Send text chunks\n    ws.send(json.dumps({\n        \"text\": \"Hello, welcome to our reception.\"\n    }))\n\n    # Signal end of input\n    ws.send(json.dumps({\n        \"text\": \"\"\n    }))\n\nws = websocket.WebSocketApp(\n    f\"wss://api.elevenlabs.io/v1/text-to-speech/{VOICE_ID}/stream-input\",\n    on_message=on_message,\n    on_open=on_open\n)\nws.run_forever()\n```\n\n---\n\n## Voice Cloning\n\n### Instant Voice Cloning (IVC)\n\n**Endpoint:** `POST /v1/voices/add`\n\nCreate a voice clone from audio samples instantly.\n\n#### Requirements\n- Minimum 1 minute of clear audio\n- Recommended 3-5 minutes for best quality\n- Clean audio without background noise\n- Single speaker only\n- Supported formats: mp3, wav, m4a\n\n#### Request\n\n```python\nimport requests\n\nurl = \"https://api.elevenlabs.io/v1/voices/add\"\n\nheaders = {\n    \"xi-api-key\": API_KEY\n}\n\nfiles = [\n    (\"files\", (\"sample1.mp3\", open(\"sample1.mp3\", \"rb\"), \"audio/mpeg\")),\n    (\"files\", (\"sample2.mp3\", open(\"sample2.mp3\", \"rb\"), \"audio/mpeg\"))\n]\n\ndata = {\n    \"name\": \"Custom Receptionist Voice\",\n    \"description\": \"Professional, warm voice for reception duties\",\n    \"labels\": '{\"accent\": \"american\", \"gender\": \"female\", \"age\": \"middle-aged\"}'\n}\n\nresponse = requests.post(url, headers=headers, files=files, data=data)\nvoice_id = response.json()[\"voice_id\"]\n```\n\n### Professional Voice Cloning (PVC)\n\nHigher quality cloning requiring verification process.\n\n**Endpoint:** `POST /v1/voices/add/professional`\n\n#### Requirements\n- Minimum 30 minutes of high-quality audio\n- Verification of voice ownership\n- Takes several hours to process\n- Significantly better quality than IVC\n\n#### Sample Quality Guidelines\n\n| Aspect | Requirement |\n|--------|-------------|\n| Audio Quality | Studio-grade or high-quality recording |\n| Background Noise | Minimal to none |\n| Speech Content | Natural, varied speech patterns |\n| Emotions | Include various emotional ranges |\n| Duration | 30+ minutes total |\n\n### Voice Library\n\n**Endpoint:** `GET /v1/voices`\n\nList all available voices:\n\n```python\nresponse = requests.get(\n    \"https://api.elevenlabs.io/v1/voices\",\n    headers={\"xi-api-key\": API_KEY}\n)\nvoices = response.json()[\"voices\"]\n```\n\n### Delete Voice\n\n**Endpoint:** `DELETE /v1/voices/{voice_id}`\n\n---\n\n## Conversational AI\n\n### Overview\nElevenLabs Conversational AI enables real-time voice interactions with AI agents.\n\n### Agent Configuration\n\n**Endpoint:** `POST /v1/convai/agents`\n\n```python\nagent_config = {\n    \"name\": \"Reception Assistant\",\n    \"voice_id\": \"21m00Tcm4TlvDq8ikWAM\",\n    \"model\": {\n        \"provider\": \"openai\",\n        \"model\": \"gpt-4\"\n    },\n    \"first_message\": \"Hello, thank you for calling. How may I assist you today?\",\n    \"system_prompt\": \"You are a professional receptionist...\",\n    \"language\": \"en\",\n    \"max_duration_seconds\": 600\n}\n```\n\n### Conversation Session\n\n**WebSocket Endpoint:** `wss://api.elevenlabs.io/v1/convai/conversation`\n\n#### Session Flow\n1. Connect to WebSocket with agent configuration\n2. Send user audio chunks (PCM format)\n3. Receive AI audio responses\n4. Handle interruptions and turn-taking\n5. Close session when complete\n\n#### Example Session\n\n```python\nimport asyncio\nimport websockets\nimport json\n\nasync def conversation_session():\n    uri = \"wss://api.elevenlabs.io/v1/convai/conversation\"\n\n    async with websockets.connect(uri) as websocket:\n        # Initialize session\n        await websocket.send(json.dumps({\n            \"type\": \"session.start\",\n            \"agent_id\": \"your-agent-id\",\n            \"xi_api_key\": API_KEY\n        }))\n\n        # Handle messages\n        async for message in websocket:\n            data = json.loads(message)\n            if data[\"type\"] == \"audio\":\n                play_audio(data[\"audio\"])\n            elif data[\"type\"] == \"transcript\":\n                print(f\"AI: {data['text']}\")\n```\n\n### Turn Detection\nElevenLabs supports:\n- **Voice Activity Detection (VAD):** Automatic turn detection\n- **Push-to-Talk:** Manual control\n- **Server-side VAD:** Lower latency option\n\n---\n\n## Models and Flash v2.5\n\n### Available Models\n\n| Model ID | Name | Latency | Quality | Languages |\n|----------|------|---------|---------|-----------|\n| `eleven_flash_v2_5` | Flash v2.5 | Ultra-low (~75ms) | High | 32 |\n| `eleven_turbo_v2_5` | Turbo v2.5 | Low (~150ms) | Very High | 32 |\n| `eleven_multilingual_v2` | Multilingual v2 | Medium | Excellent | 29 |\n| `eleven_monolingual_v1` | Monolingual v1 | Medium | Good | 1 (English) |\n\n### Flash v2.5 Specifics\n\n**Model ID:** `eleven_flash_v2_5`\n\n#### Key Features\n- **Ultra-Low Latency:** ~75ms time-to-first-byte\n- **32 Languages:** Full multilingual support\n- **Optimized for Real-Time:** Designed for conversational AI\n- **High Quality:** Maintains natural prosody\n- **Cost Effective:** Lower cost per character than Turbo\n\n#### When to Use Flash v2.5\n- Real-time voice assistants\n- Interactive voice response (IVR) systems\n- Live translation applications\n- Gaming and VR applications\n- Any application requiring sub-100ms latency\n\n#### Flash v2.5 Configuration\n\n```python\ndata = {\n    \"text\": \"How may I help you today?\",\n    \"model_id\": \"eleven_flash_v2_5\",\n    \"voice_settings\": {\n        \"stability\": 0.5,\n        \"similarity_boost\": 0.8,\n        \"style\": 0.0,\n        \"use_speaker_boost\": true\n    },\n    \"output_format\": \"pcm_24000\"\n}\n```\n\n#### Supported Languages (Flash v2.5)\nEnglish, Spanish, French, German, Italian, Portuguese, Polish, Hindi, Arabic, Japanese, Korean, Chinese, Dutch, Turkish, Swedish, Indonesian, Filipino, Malay, Romanian, Ukrainian, Greek, Czech, Danish, Finnish, Bulgarian, Croatian, Slovak, Tamil, and more.\n\n### Turbo v2.5 vs Flash v2.5\n\n| Aspect | Flash v2.5 | Turbo v2.5 |\n|--------|------------|------------|\n| Latency | ~75ms | ~150ms |\n| Quality | High | Very High |\n| Emotional Range | Good | Excellent |\n| Complex Prosody | Good | Excellent |\n| Cost | Lower | Higher |\n| Best For | Real-time | Quality-focused |\n\n---\n\n## Latency Optimization\n\n### Time-to-First-Byte (TTFB) Targets\n\n| Use Case | Target TTFB |\n|----------|-------------|\n| Real-time conversation | < 100ms |\n| IVR systems | < 200ms |\n| Pre-rendered content | < 500ms |\n\n### Optimization Strategies\n\n#### 1. Model Selection\n```python\n# For lowest latency\nmodel_id = \"eleven_flash_v2_5\"\n```\n\n#### 2. Output Format Selection\n```python\n# PCM is faster than encoded formats\noutput_format = \"pcm_24000\"  # Faster\n# vs\noutput_format = \"mp3_44100_128\"  # Slower\n```\n\n#### 3. Use Streaming Endpoints\n```python\n# Always use streaming for real-time\nurl = f\"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}/stream\"\n```\n\n#### 4. WebSocket for Continuous Sessions\n```python\n# Eliminate HTTP overhead with WebSocket\nws_url = f\"wss://api.elevenlabs.io/v1/text-to-speech/{voice_id}/stream-input\"\n```\n\n#### 5. Text Chunking\n```python\n# Send text in chunks for faster TTFB\nsentences = text.split(\". \")\nfor sentence in sentences:\n    await ws.send(json.dumps({\"text\": sentence + \". \"}))\n```\n\n#### 6. Regional Endpoints\nUse the closest regional endpoint to minimize network latency.\n\n#### 7. Connection Pooling\n```python\n# Reuse connections\nsession = requests.Session()\nsession.headers.update({\"xi-api-key\": API_KEY})\n```\n\n#### 8. Optimize Voice Settings\n```python\n# Lower stability = faster generation\nvoice_settings = {\n    \"stability\": 0.3,  # Lower for speed\n    \"similarity_boost\": 0.75\n}\n```\n\n### Latency Measurement\n\n```python\nimport time\n\nstart = time.time()\nresponse = requests.post(url, json=data, headers=headers, stream=True)\nfirst_chunk = next(response.iter_content(chunk_size=1024))\nttfb = time.time() - start\nprint(f\"Time to first byte: {ttfb * 1000:.0f}ms\")\n```\n\n---\n\n## Pricing Structure\n\n### Pricing Tiers (as of 2024/2025)\n\n| Plan | Monthly Cost | Characters | Cost per 1K chars |\n|------|--------------|------------|-------------------|\n| Free | $0 | 10,000 | N/A |\n| Starter | $5 | 30,000 | ~$0.17 |\n| Creator | $22 | 100,000 | ~$0.22 |\n| Pro | $99 | 500,000 | ~$0.20 |\n| Scale | $330 | 2,000,000 | ~$0.17 |\n| Enterprise | Custom | Custom | Custom |\n\n### Character Calculation\n- Each character of text counts towards quota\n- Spaces and punctuation are counted\n- Regenerations count as new generations\n\n### Cost Estimation\n\n```python\ndef estimate_cost(text, price_per_1k=0.20):\n    chars = len(text)\n    cost = (chars / 1000) * price_per_1k\n    return cost\n\n# Example\ntext = \"Hello, welcome to our reception.\"\ncost = estimate_cost(text)\nprint(f\"Cost: ${cost:.4f}\")\n```\n\n### Minutes Estimation\n- Average speaking rate: ~150 words/minute\n- Average characters per word: ~5\n- Approximately 750 characters per minute\n\n```python\ndef chars_to_minutes(chars):\n    return chars / 750\n\ndef minutes_to_chars(minutes):\n    return minutes * 750\n```\n\n### Professional Voice Cloning Pricing\n- Included in Pro and higher plans\n- Additional cost on lower tiers\n- One-time generation with unlimited usage\n\n---\n\n## VAPI Integration\n\n### Overview\nVAPI (Voice API) is a platform for building voice AI agents that integrates with ElevenLabs for text-to-speech.\n\n### Configuration\n\n```python\nvapi_config = {\n    \"voice\": {\n        \"provider\": \"11labs\",\n        \"voiceId\": \"21m00Tcm4TlvDq8ikWAM\",\n        \"model\": \"eleven_flash_v2_5\",\n        \"stability\": 0.5,\n        \"similarityBoost\": 0.75,\n        \"optimizeStreamingLatency\": 4  # 0-4, higher = lower latency\n    }\n}\n```\n\n### VAPI Assistant with ElevenLabs\n\n```python\nassistant_config = {\n    \"name\": \"Receptionist\",\n    \"model\": {\n        \"provider\": \"openai\",\n        \"model\": \"gpt-4-turbo\",\n        \"temperature\": 0.7\n    },\n    \"voice\": {\n        \"provider\": \"11labs\",\n        \"voiceId\": \"YOUR_VOICE_ID\",\n        \"model\": \"eleven_flash_v2_5\",\n        \"stability\": 0.5,\n        \"similarityBoost\": 0.8,\n        \"optimizeStreamingLatency\": 4\n    },\n    \"firstMessage\": \"Hello, thank you for calling. How may I assist you?\",\n    \"transcriber\": {\n        \"provider\": \"deepgram\",\n        \"model\": \"nova-2\"\n    }\n}\n```\n\n### Optimize Streaming Latency Setting\n\n| Level | Description | Use Case |\n|-------|-------------|----------|\n| 0 | Default | General use |\n| 1 | Slight optimization | Balanced |\n| 2 | Moderate | Interactive |\n| 3 | Aggressive | Real-time |\n| 4 | Maximum | Ultra-low latency |\n\n### VAPI + ElevenLabs Best Practices\n\n1. **Use Flash v2.5 Model**\n   ```python\n   \"model\": \"eleven_flash_v2_5\"\n   ```\n\n2. **Set Latency Optimization to Maximum**\n   ```python\n   \"optimizeStreamingLatency\": 4\n   ```\n\n3. **Use PCM Output Format**\n   ```python\n   \"outputFormat\": \"pcm_24000\"\n   ```\n\n4. **Configure Appropriate Voice Settings**\n   ```python\n   \"stability\": 0.5,  # Balance between consistency and expressiveness\n   \"similarityBoost\": 0.75  # Voice matching accuracy\n   ```\n\n### Webhook Integration\n\n```python\n# VAPI webhook for call events\n@app.post(\"/vapi/webhook\")\nasync def vapi_webhook(request: Request):\n    data = await request.json()\n\n    if data[\"type\"] == \"call-started\":\n        # Log call initiation\n        pass\n    elif data[\"type\"] == \"transcript\":\n        # Process transcript\n        pass\n    elif data[\"type\"] == \"call-ended\":\n        # Handle call completion\n        pass\n```\n\n---\n\n## Best Practices for Low-Latency Voice AI\n\n### 1. Architecture Design\n\n```\nUser Audio -> STT -> LLM -> TTS -> User\n                      |\n                 [Minimize each step]\n```\n\n### 2. Streaming Everything\n- Stream STT transcription\n- Stream LLM responses\n- Stream TTS audio\n- Never wait for complete outputs\n\n### 3. Model Selection Priority\n\n```python\n# Priority order for low latency\nmodels_by_latency = [\n    \"eleven_flash_v2_5\",    # First choice\n    \"eleven_turbo_v2_5\",    # Second choice\n    \"eleven_multilingual_v2\" # Third choice\n]\n```\n\n### 4. Connection Management\n\n```python\n# Persistent WebSocket connection\nclass VoiceConnection:\n    def __init__(self):\n        self.ws = None\n        self.connected = False\n\n    async def ensure_connected(self):\n        if not self.connected:\n            self.ws = await websockets.connect(WS_URL)\n            self.connected = True\n        return self.ws\n```\n\n### 5. Pre-warm Connections\n```python\n# Pre-establish connection before needed\nasync def prewarm():\n    await voice_connection.ensure_connected()\n\n# Call during application startup\nasyncio.create_task(prewarm())\n```\n\n### 6. Sentence-Level Streaming\n\n```python\nasync def stream_response(llm_response):\n    buffer = \"\"\n    for token in llm_response:\n        buffer += token\n\n        # Send complete sentences immediately\n        if token in \".!?\":\n            await tts_stream(buffer)\n            buffer = \"\"\n```\n\n### 7. Parallel Processing\n\n```python\nasync def process_turn(user_audio):\n    # Run STT and prepare TTS in parallel\n    transcript_task = asyncio.create_task(stt(user_audio))\n    tts_ready_task = asyncio.create_task(prepare_tts_connection())\n\n    transcript = await transcript_task\n    tts_connection = await tts_ready_task\n\n    # Stream LLM to TTS\n    async for llm_chunk in get_llm_response(transcript):\n        await tts_connection.send(llm_chunk)\n```\n\n### 8. Audio Format Optimization\n\n```python\n# Optimal format for telephony\noutput_format = \"ulaw_8000\"  # For Twilio/telephony\n\n# Optimal format for web\noutput_format = \"pcm_24000\"  # For web applications\n```\n\n### 9. Interruption Handling\n\n```python\nasync def handle_interruption():\n    # Immediately stop TTS playback\n    await stop_audio_playback()\n\n    # Clear audio buffer\n    audio_buffer.clear()\n\n    # Signal TTS to stop generating\n    await tts_connection.send({\"type\": \"interrupt\"})\n```\n\n### 10. Caching Common Responses\n\n```python\n# Cache frequently used phrases\nGREETING_CACHE = {\n    \"greeting\": pregenerate_audio(\"Hello, how may I help you?\"),\n    \"hold\": pregenerate_audio(\"Please hold while I check that for you.\"),\n    \"goodbye\": pregenerate_audio(\"Thank you for calling. Goodbye!\")\n}\n```\n\n### 11. Error Recovery\n\n```python\nasync def robust_tts(text, max_retries=3):\n    for attempt in range(max_retries):\n        try:\n            return await tts_stream(text)\n        except ConnectionError:\n            await asyncio.sleep(0.1 * attempt)\n            await reconnect()\n    raise TTSError(\"Failed after retries\")\n```\n\n### 12. Monitoring and Metrics\n\n```python\n# Track key latency metrics\nmetrics = {\n    \"stt_latency\": [],\n    \"llm_latency\": [],\n    \"tts_ttfb\": [],\n    \"total_turn_time\": []\n}\n\ndef log_latency(metric_name, value):\n    metrics[metric_name].append(value)\n    if value > THRESHOLD:\n        alert(f\"High latency: {metric_name} = {value}ms\")\n```\n\n---\n\n## Error Handling\n\n### Common Error Codes\n\n| Code | Meaning | Resolution |\n|------|---------|------------|\n| 401 | Unauthorized | Check API key |\n| 422 | Invalid request | Validate parameters |\n| 429 | Rate limited | Implement backoff |\n| 500 | Server error | Retry with backoff |\n\n### Retry Logic\n\n```python\nimport time\nfrom functools import wraps\n\ndef retry_with_backoff(max_retries=3, base_delay=1):\n    def decorator(func):\n        @wraps(func)\n        async def wrapper(*args, **kwargs):\n            for attempt in range(max_retries):\n                try:\n                    return await func(*args, **kwargs)\n                except Exception as e:\n                    if attempt == max_retries - 1:\n                        raise\n                    delay = base_delay * (2 ** attempt)\n                    await asyncio.sleep(delay)\n        return wrapper\n    return decorator\n\n@retry_with_backoff(max_retries=3)\nasync def call_tts(text):\n    return await tts_api(text)\n```\n\n---\n\n## Rate Limits\n\n### Default Limits\n\n| Plan | Requests/Second | Concurrent |\n|------|-----------------|------------|\n| Free | 2 | 2 |\n| Starter | 5 | 3 |\n| Creator | 10 | 5 |\n| Pro | 25 | 10 |\n| Scale | 50 | 25 |\n| Enterprise | Custom | Custom |\n\n### Rate Limit Headers\n\n```\nx-ratelimit-limit: 25\nx-ratelimit-remaining: 24\nx-ratelimit-reset: 1640000000\n```\n\n### Handling Rate Limits\n\n```python\nasync def handle_rate_limit(response):\n    if response.status_code == 429:\n        reset_time = int(response.headers.get(\"x-ratelimit-reset\", 0))\n        wait_time = max(reset_time - time.time(), 1)\n        await asyncio.sleep(wait_time)\n        return True  # Retry\n    return False  # Don't retry\n```\n\n---\n\n## Quick Reference\n\n### Minimum Latency Configuration\n\n```python\nOPTIMAL_CONFIG = {\n    \"model_id\": \"eleven_flash_v2_5\",\n    \"output_format\": \"pcm_24000\",\n    \"voice_settings\": {\n        \"stability\": 0.5,\n        \"similarity_boost\": 0.75\n    },\n    \"optimize_streaming_latency\": 4\n}\n```\n\n### Essential Endpoints\n\n| Purpose | Endpoint |\n|---------|----------|\n| TTS | `POST /v1/text-to-speech/{voice_id}` |\n| TTS Stream | `POST /v1/text-to-speech/{voice_id}/stream` |\n| TTS WebSocket | `wss://api.elevenlabs.io/v1/text-to-speech/{voice_id}/stream-input` |\n| Voice Clone | `POST /v1/voices/add` |\n| List Voices | `GET /v1/voices` |\n| User Info | `GET /v1/user` |\n\n### SDK Installation\n\n```bash\n# Python\npip install elevenlabs\n\n# Node.js\nnpm install elevenlabs\n```\n\n### Python SDK Usage\n\n```python\nfrom elevenlabs import ElevenLabs, stream\n\nclient = ElevenLabs(api_key=\"your-api-key\")\n\n# Generate and stream audio\naudio_stream = client.text_to_speech.convert_as_stream(\n    voice_id=\"21m00Tcm4TlvDq8ikWAM\",\n    text=\"Hello, how may I help you?\",\n    model_id=\"eleven_flash_v2_5\"\n)\n\nstream(audio_stream)\n```\n\n---\n\n## Additional Resources\n\n- **API Reference:** https://elevenlabs.io/docs/api-reference\n- **Python SDK:** https://github.com/elevenlabs/elevenlabs-python\n- **Node.js SDK:** https://github.com/elevenlabs/elevenlabs-js\n- **Discord Community:** https://discord.gg/elevenlabs\n- **Status Page:** https://status.elevenlabs.io\n\n---\n\n## Document Revision History\n\n| Version | Date | Changes |\n|---------|------|---------|\n| 1.0 | January 2026 | Initial comprehensive documentation |\n\n---\n\n*This document is maintained for the ReceptionistAI project. For the latest API updates, always refer to the official ElevenLabs documentation.*\n","structuredPatch":[],"originalFile":null},"sourceToolAssistantUUID":"f0b10d25-882a-45fd-88e6-594579e5c65c"}
{"parentUuid":"65387f9b-c632-40e5-9b19-b92c5a599a4c","isSidechain":true,"userType":"external","cwd":"E:\\","sessionId":"9d44f1f1-4809-4eee-be0f-60bca50c3a90","version":"2.1.8","gitBranch":"","agentId":"a71881f","slug":"quiet-herding-bubble","message":{"model":"claude-opus-4-5-20251101","id":"msg_01PDCV3FxfU3TVXyhTozUng3","type":"message","role":"assistant","content":[{"type":"text","text":"I have created the comprehensive ElevenLabs API documentation. Let me verify the file was created correctly and provide a summary."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":7675,"cache_read_input_tokens":16120,"cache_creation":{"ephemeral_5m_input_tokens":7675,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard"}},"requestId":"req_011CXFhJckcCGL5AJZoAj7fL","type":"assistant","uuid":"b1a4817d-3b30-4e4b-b68d-81663a031da5","timestamp":"2026-01-18T23:16:37.307Z"}
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{"parentUuid":"7bab7652-2547-4462-b1b3-0e3249233c52","isSidechain":true,"userType":"external","cwd":"E:\\","sessionId":"9d44f1f1-4809-4eee-be0f-60bca50c3a90","version":"2.1.8","gitBranch":"","agentId":"a71881f","slug":"quiet-herding-bubble","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012vBqgYVMRVgi5RY8r4yQqu","type":"tool_result","content":"     1→# ElevenLabs API Mastery Guide\n     2→\n     3→## Comprehensive Documentation for Voice AI Integration\n     4→\n     5→**Version:** 1.0\n     6→**Last Updated:** January 2026\n     7→**Purpose:** Complete reference for ElevenLabs API integration in ReceptionistAI\n     8→\n     9→---\n    10→\n    11→## Table of Contents\n    12→\n    13→1. [API Overview](#api-overview)\n    14→2. [Authentication](#authentication)\n    15→3. [Text-to-Speech Endpoints](#text-to-speech-endpoints)\n    16→4. [Voice Cloning](#voice-cloning)\n    17→5. [Conversational AI](#conversational-ai)\n    18→6. [Models and Flash v2.5](#models-and-flash-v25)\n    19→7. [Latency Optimization](#latency-optimization)\n    20→8. [Pricing Structure](#pricing-structure)\n    21→9. [VAPI Integration](#vapi-integration)\n    22→10. [Best Practices for Low-Latency Voice AI](#best-practices-for-low-latency-voice-ai)\n    23→11. [Error Handling](#error-handling)\n    24→12. [Rate Limits](#rate-limits)\n    25→\n    26→---\n    27→\n    28→## API Overview\n    29→\n    30→### Base URL\n    31→```\n    32→https://api.elevenlabs.io/v1\n    33→```\n    34→\n    35→### Core Capabilities\n    36→- **Text-to-Speech (TTS):** Convert text to natural-sounding speech\n    37→- **Voice Cloning:** Create custom voices from audio samples\n    38→- **Speech-to-Speech:** Transform voice characteristics\n    39→- **Conversational AI:** Real-time voice interactions\n    40→- **Audio Isolation:** Remove background noise\n    41→- **Voice Design:** Generate synthetic voices from descriptions\n    42→\n    43→---\n    44→\n    45→## Authentication\n    46→\n    47→### API Key Authentication\n    48→All requests require an API key passed in the header:\n    49→\n    50→```bash\n    51→curl -X POST \"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}\" \\\n    52→  -H \"xi-api-key: YOUR_API_KEY\" \\\n    53→  -H \"Content-Type: application/json\"\n    54→```\n    55→\n    56→### Header Format\n    57→```\n    58→xi-api-key: <your-api-key>\n    59→```\n    60→\n    61→### Obtaining API Key\n    62→1. Create account at elevenlabs.io\n    63→2. Navigate to Profile Settings\n    64→3. Generate API key from the API section\n    65→\n    66→---\n    67→\n    68→## Text-to-Speech Endpoints\n    69→\n    70→### Standard TTS Endpoint (Non-Streaming)\n    71→\n    72→**Endpoint:** `POST /v1/text-to-speech/{voice_id}`\n    73→\n    74→**Description:** Converts text to speech and returns the complete audio file.\n    75→\n    76→#### Request Parameters\n    77→\n    78→| Parameter | Type | Required | Description |\n    79→|-----------|------|----------|-------------|\n    80→| `voice_id` | string | Yes | The voice ID to use for synthesis |\n    81→| `text` | string | Yes | The text to convert (max 5000 chars) |\n    82→| `model_id` | string | No | Model to use (default: eleven_monolingual_v1) |\n    83→| `voice_settings` | object | No | Voice configuration settings |\n    84→| `output_format` | string | No | Audio format (mp3_44100_128, pcm_16000, etc.) |\n    85→\n    86→#### Voice Settings Object\n    87→\n    88→```json\n    89→{\n    90→  \"stability\": 0.5,\n    91→  \"similarity_boost\": 0.75,\n    92→  \"style\": 0.0,\n    93→  \"use_speaker_boost\": true\n    94→}\n    95→```\n    96→\n    97→| Setting | Range | Description |\n    98→|---------|-------|-------------|\n    99→| `stability` | 0.0-1.0 | Higher = more consistent, lower = more expressive |\n   100→| `similarity_boost` | 0.0-1.0 | How closely to match original voice |\n   101→| `style` | 0.0-1.0 | Style exaggeration (only for v2 models) |\n   102→| `use_speaker_boost` | boolean | Boost clarity for speaker recognition |\n   103→\n   104→#### Example Request\n   105→\n   106→```python\n   107→import requests\n   108→\n   109→VOICE_ID = \"21m00Tcm4TlvDq8ikWAM\"  # Rachel voice\n   110→API_KEY = \"your-api-key\"\n   111→\n   112→url = f\"https://api.elevenlabs.io/v1/text-to-speech/{VOICE_ID}\"\n   113→\n   114→headers = {\n   115→    \"xi-api-key\": API_KEY,\n   116→    \"Content-Type\": \"application/json\"\n   117→}\n   118→\n   119→data = {\n   120→    \"text\": \"Hello, welcome to our reception. How may I assist you today?\",\n   121→    \"model_id\": \"eleven_flash_v2_5\",\n   122→    \"voice_settings\": {\n   123→        \"stability\": 0.5,\n   124→        \"similarity_boost\": 0.75\n   125→    }\n   126→}\n   127→\n   128→response = requests.post(url, json=data, headers=headers)\n   129→\n   130→with open(\"output.mp3\", \"wb\") as f:\n   131→    f.write(response.content)\n   132→```\n   133→\n   134→#### Output Formats\n   135→\n   136→| Format | Description | Use Case |\n   137→|--------|-------------|----------|\n   138→| `mp3_44100_128` | MP3 at 44.1kHz, 128kbps | General use, good quality |\n   139→| `mp3_44100_64` | MP3 at 44.1kHz, 64kbps | Smaller files |\n   140→| `pcm_16000` | Raw PCM at 16kHz | Real-time streaming |\n   141→| `pcm_22050` | Raw PCM at 22.05kHz | Higher quality streaming |\n   142→| `pcm_24000` | Raw PCM at 24kHz | Telephony integration |\n   143→| `pcm_44100` | Raw PCM at 44.1kHz | High quality streaming |\n   144→| `ulaw_8000` | mu-law at 8kHz | Telephony (Twilio, VAPI) |\n   145→\n   146→---\n   147→\n   148→### Streaming TTS Endpoint\n   149→\n   150→**Endpoint:** `POST /v1/text-to-speech/{voice_id}/stream`\n   151→\n   152→**Description:** Returns audio as a stream for real-time playback.\n   153→\n   154→#### Key Differences from Standard Endpoint\n   155→- Returns chunked transfer encoding\n   156→- Enables playback before generation completes\n   157→- Critical for low-latency applications\n   158→\n   159→#### Streaming Request\n   160→\n   161→```python\n   162→import requests\n   163→\n   164→url = f\"https://api.elevenlabs.io/v1/text-to-speech/{VOICE_ID}/stream\"\n   165→\n   166→headers = {\n   167→    \"xi-api-key\": API_KEY,\n   168→    \"Content-Type\": \"application/json\"\n   169→}\n   170→\n   171→data = {\n   172→    \"text\": \"Welcome to our service. How can I help you today?\",\n   173→    \"model_id\": \"eleven_flash_v2_5\",\n   174→    \"output_format\": \"pcm_24000\"\n   175→}\n   176→\n   177→# Stream response\n   178→with requests.post(url, json=data, headers=headers, stream=True) as response:\n   179→    for chunk in response.iter_content(chunk_size=1024):\n   180→        # Process audio chunks in real-time\n   181→        play_audio_chunk(chunk)\n   182→```\n   183→\n   184→#### WebSocket Streaming\n   185→\n   186→**Endpoint:** `wss://api.elevenlabs.io/v1/text-to-speech/{voice_id}/stream-input`\n   187→\n   188→For ultra-low latency, use WebSocket streaming with input streaming:\n   189→\n   190→```python\n   191→import websocket\n   192→import json\n   193→import base64\n   194→\n   195→def on_message(ws, message):\n   196→    data = json.loads(message)\n   197→    if data.get(\"audio\"):\n   198→        audio_chunk = base64.b64decode(data[\"audio\"])\n   199→        play_audio(audio_chunk)\n   200→\n   201→def on_open(ws):\n   202→    # Initial configuration\n   203→    ws.send(json.dumps({\n   204→        \"text\": \" \",\n   205→        \"voice_settings\": {\n   206→            \"stability\": 0.5,\n   207→            \"similarity_boost\": 0.75\n   208→        },\n   209→        \"xi_api_key\": API_KEY,\n   210→        \"model_id\": \"eleven_flash_v2_5\",\n   211→        \"output_format\": \"pcm_24000\"\n   212→    }))\n   213→\n   214→    # Send text chunks\n   215→    ws.send(json.dumps({\n   216→        \"text\": \"Hello, welcome to our reception.\"\n   217→    }))\n   218→\n   219→    # Signal end of input\n   220→    ws.send(json.dumps({\n   221→        \"text\": \"\"\n   222→    }))\n   223→\n   224→ws = websocket.WebSocketApp(\n   225→    f\"wss://api.elevenlabs.io/v1/text-to-speech/{VOICE_ID}/stream-input\",\n   226→    on_message=on_message,\n   227→    on_open=on_open\n   228→)\n   229→ws.run_forever()\n   230→```\n   231→\n   232→---\n   233→\n   234→## Voice Cloning\n   235→\n   236→### Instant Voice Cloning (IVC)\n   237→\n   238→**Endpoint:** `POST /v1/voices/add`\n   239→\n   240→Create a voice clone from audio samples instantly.\n   241→\n   242→#### Requirements\n   243→- Minimum 1 minute of clear audio\n   244→- Recommended 3-5 minutes for best quality\n   245→- Clean audio without background noise\n   246→- Single speaker only\n   247→- Supported formats: mp3, wav, m4a\n   248→\n   249→#### Request\n   250→\n   251→```python\n   252→import requests\n   253→\n   254→url = \"https://api.elevenlabs.io/v1/voices/add\"\n   255→\n   256→headers = {\n   257→    \"xi-api-key\": API_KEY\n   258→}\n   259→\n   260→files = [\n   261→    (\"files\", (\"sample1.mp3\", open(\"sample1.mp3\", \"rb\"), \"audio/mpeg\")),\n   262→    (\"files\", (\"sample2.mp3\", open(\"sample2.mp3\", \"rb\"), \"audio/mpeg\"))\n   263→]\n   264→\n   265→data = {\n   266→    \"name\": \"Custom Receptionist Voice\",\n   267→    \"description\": \"Professional, warm voice for reception duties\",\n   268→    \"labels\": '{\"accent\": \"american\", \"gender\": \"female\", \"age\": \"middle-aged\"}'\n   269→}\n   270→\n   271→response = requests.post(url, headers=headers, files=files, data=data)\n   272→voice_id = response.json()[\"voice_id\"]\n   273→```\n   274→\n   275→### Professional Voice Cloning (PVC)\n   276→\n   277→Higher quality cloning requiring verification process.\n   278→\n   279→**Endpoint:** `POST /v1/voices/add/professional`\n   280→\n   281→#### Requirements\n   282→- Minimum 30 minutes of high-quality audio\n   283→- Verification of voice ownership\n   284→- Takes several hours to process\n   285→- Significantly better quality than IVC\n   286→\n   287→#### Sample Quality Guidelines\n   288→\n   289→| Aspect | Requirement |\n   290→|--------|-------------|\n   291→| Audio Quality | Studio-grade or high-quality recording |\n   292→| Background Noise | Minimal to none |\n   293→| Speech Content | Natural, varied speech patterns |\n   294→| Emotions | Include various emotional ranges |\n   295→| Duration | 30+ minutes total |\n   296→\n   297→### Voice Library\n   298→\n   299→**Endpoint:** `GET /v1/voices`\n   300→\n   301→List all available voices:\n   302→\n   303→```python\n   304→response = requests.get(\n   305→    \"https://api.elevenlabs.io/v1/voices\",\n   306→    headers={\"xi-api-key\": API_KEY}\n   307→)\n   308→voices = response.json()[\"voices\"]\n   309→```\n   310→\n   311→### Delete Voice\n   312→\n   313→**Endpoint:** `DELETE /v1/voices/{voice_id}`\n   314→\n   315→---\n   316→\n   317→## Conversational AI\n   318→\n   319→### Overview\n   320→ElevenLabs Conversational AI enables real-time voice interactions with AI agents.\n   321→\n   322→### Agent Configuration\n   323→\n   324→**Endpoint:** `POST /v1/convai/agents`\n   325→\n   326→```python\n   327→agent_config = {\n   328→    \"name\": \"Reception Assistant\",\n   329→    \"voice_id\": \"21m00Tcm4TlvDq8ikWAM\",\n   330→    \"model\": {\n   331→        \"provider\": \"openai\",\n   332→        \"model\": \"gpt-4\"\n   333→    },\n   334→    \"first_message\": \"Hello, thank you for calling. How may I assist you today?\",\n   335→    \"system_prompt\": \"You are a professional receptionist...\",\n   336→    \"language\": \"en\",\n   337→    \"max_duration_seconds\": 600\n   338→}\n   339→```\n   340→\n   341→### Conversation Session\n   342→\n   343→**WebSocket Endpoint:** `wss://api.elevenlabs.io/v1/convai/conversation`\n   344→\n   345→#### Session Flow\n   346→1. Connect to WebSocket with agent configuration\n   347→2. Send user audio chunks (PCM format)\n   348→3. Receive AI audio responses\n   349→4. Handle interruptions and turn-taking\n   350→5. Close session when complete\n   351→\n   352→#### Example Session\n   353→\n   354→```python\n   355→import asyncio\n   356→import websockets\n   357→import json\n   358→\n   359→async def conversation_session():\n   360→    uri = \"wss://api.elevenlabs.io/v1/convai/conversation\"\n   361→\n   362→    async with websockets.connect(uri) as websocket:\n   363→        # Initialize session\n   364→        await websocket.send(json.dumps({\n   365→            \"type\": \"session.start\",\n   366→            \"agent_id\": \"your-agent-id\",\n   367→            \"xi_api_key\": API_KEY\n   368→        }))\n   369→\n   370→        # Handle messages\n   371→        async for message in websocket:\n   372→            data = json.loads(message)\n   373→            if data[\"type\"] == \"audio\":\n   374→                play_audio(data[\"audio\"])\n   375→            elif data[\"type\"] == \"transcript\":\n   376→                print(f\"AI: {data['text']}\")\n   377→```\n   378→\n   379→### Turn Detection\n   380→ElevenLabs supports:\n   381→- **Voice Activity Detection (VAD):** Automatic turn detection\n   382→- **Push-to-Talk:** Manual control\n   383→- **Server-side VAD:** Lower latency option\n   384→\n   385→---\n   386→\n   387→## Models and Flash v2.5\n   388→\n   389→### Available Models\n   390→\n   391→| Model ID | Name | Latency | Quality | Languages |\n   392→|----------|------|---------|---------|-----------|\n   393→| `eleven_flash_v2_5` | Flash v2.5 | Ultra-low (~75ms) | High | 32 |\n   394→| `eleven_turbo_v2_5` | Turbo v2.5 | Low (~150ms) | Very High | 32 |\n   395→| `eleven_multilingual_v2` | Multilingual v2 | Medium | Excellent | 29 |\n   396→| `eleven_monolingual_v1` | Monolingual v1 | Medium | Good | 1 (English) |\n   397→\n   398→### Flash v2.5 Specifics\n   399→\n   400→**Model ID:** `eleven_flash_v2_5`\n   401→\n   402→#### Key Features\n   403→- **Ultra-Low Latency:** ~75ms time-to-first-byte\n   404→- **32 Languages:** Full multilingual support\n   405→- **Optimized for Real-Time:** Designed for conversational AI\n   406→- **High Quality:** Maintains natural prosody\n   407→- **Cost Effective:** Lower cost per character than Turbo\n   408→\n   409→#### When to Use Flash v2.5\n   410→- Real-time voice assistants\n   411→- Interactive voice response (IVR) systems\n   412→- Live translation applications\n   413→- Gaming and VR applications\n   414→- Any application requiring sub-100ms latency\n   415→\n   416→#### Flash v2.5 Configuration\n   417→\n   418→```python\n   419→data = {\n   420→    \"text\": \"How may I help you today?\",\n   421→    \"model_id\": \"eleven_flash_v2_5\",\n   422→    \"voice_settings\": {\n   423→        \"stability\": 0.5,\n   424→        \"similarity_boost\": 0.8,\n   425→        \"style\": 0.0,\n   426→        \"use_speaker_boost\": true\n   427→    },\n   428→    \"output_format\": \"pcm_24000\"\n   429→}\n   430→```\n   431→\n   432→#### Supported Languages (Flash v2.5)\n   433→English, Spanish, French, German, Italian, Portuguese, Polish, Hindi, Arabic, Japanese, Korean, Chinese, Dutch, Turkish, Swedish, Indonesian, Filipino, Malay, Romanian, Ukrainian, Greek, Czech, Danish, Finnish, Bulgarian, Croatian, Slovak, Tamil, and more.\n   434→\n   435→### Turbo v2.5 vs Flash v2.5\n   436→\n   437→| Aspect | Flash v2.5 | Turbo v2.5 |\n   438→|--------|------------|------------|\n   439→| Latency | ~75ms | ~150ms |\n   440→| Quality | High | Very High |\n   441→| Emotional Range | Good | Excellent |\n   442→| Complex Prosody | Good | Excellent |\n   443→| Cost | Lower | Higher |\n   444→| Best For | Real-time | Quality-focused |\n   445→\n   446→---\n   447→\n   448→## Latency Optimization\n   449→\n   450→### Time-to-First-Byte (TTFB) Targets\n   451→\n   452→| Use Case | Target TTFB |\n   453→|----------|-------------|\n   454→| Real-time conversation | < 100ms |\n   455→| IVR systems | < 200ms |\n   456→| Pre-rendered content | < 500ms |\n   457→\n   458→### Optimization Strategies\n   459→\n   460→#### 1. Model Selection\n   461→```python\n   462→# For lowest latency\n   463→model_id = \"eleven_flash_v2_5\"\n   464→```\n   465→\n   466→#### 2. Output Format Selection\n   467→```python\n   468→# PCM is faster than encoded formats\n   469→output_format = \"pcm_24000\"  # Faster\n   470→# vs\n   471→output_format = \"mp3_44100_128\"  # Slower\n   472→```\n   473→\n   474→#### 3. Use Streaming Endpoints\n   475→```python\n   476→# Always use streaming for real-time\n   477→url = f\"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}/stream\"\n   478→```\n   479→\n   480→#### 4. WebSocket for Continuous Sessions\n   481→```python\n   482→# Eliminate HTTP overhead with WebSocket\n   483→ws_url = f\"wss://api.elevenlabs.io/v1/text-to-speech/{voice_id}/stream-input\"\n   484→```\n   485→\n   486→#### 5. Text Chunking\n   487→```python\n   488→# Send text in chunks for faster TTFB\n   489→sentences = text.split(\". \")\n   490→for sentence in sentences:\n   491→    await ws.send(json.dumps({\"text\": sentence + \". \"}))\n   492→```\n   493→\n   494→#### 6. Regional Endpoints\n   495→Use the closest regional endpoint to minimize network latency.\n   496→\n   497→#### 7. Connection Pooling\n   498→```python\n   499→# Reuse connections\n   500→session = requests.Session()\n   501→session.headers.update({\"xi-api-key\": API_KEY})\n   502→```\n   503→\n   504→#### 8. Optimize Voice Settings\n   505→```python\n   506→# Lower stability = faster generation\n   507→voice_settings = {\n   508→    \"stability\": 0.3,  # Lower for speed\n   509→    \"similarity_boost\": 0.75\n   510→}\n   511→```\n   512→\n   513→### Latency Measurement\n   514→\n   515→```python\n   516→import time\n   517→\n   518→start = time.time()\n   519→response = requests.post(url, json=data, headers=headers, stream=True)\n   520→first_chunk = next(response.iter_content(chunk_size=1024))\n   521→ttfb = time.time() - start\n   522→print(f\"Time to first byte: {ttfb * 1000:.0f}ms\")\n   523→```\n   524→\n   525→---\n   526→\n   527→## Pricing Structure\n   528→\n   529→### Pricing Tiers (as of 2024/2025)\n   530→\n   531→| Plan | Monthly Cost | Characters | Cost per 1K chars |\n   532→|------|--------------|------------|-------------------|\n   533→| Free | $0 | 10,000 | N/A |\n   534→| Starter | $5 | 30,000 | ~$0.17 |\n   535→| Creator | $22 | 100,000 | ~$0.22 |\n   536→| Pro | $99 | 500,000 | ~$0.20 |\n   537→| Scale | $330 | 2,000,000 | ~$0.17 |\n   538→| Enterprise | Custom | Custom | Custom |\n   539→\n   540→### Character Calculation\n   541→- Each character of text counts towards quota\n   542→- Spaces and punctuation are counted\n   543→- Regenerations count as new generations\n   544→\n   545→### Cost Estimation\n   546→\n   547→```python\n   548→def estimate_cost(text, price_per_1k=0.20):\n   549→    chars = len(text)\n   550→    cost = (chars / 1000) * price_per_1k\n   551→    return cost\n   552→\n   553→# Example\n   554→text = \"Hello, welcome to our reception.\"\n   555→cost = estimate_cost(text)\n   556→print(f\"Cost: ${cost:.4f}\")\n   557→```\n   558→\n   559→### Minutes Estimation\n   560→- Average speaking rate: ~150 words/minute\n   561→- Average characters per word: ~5\n   562→- Approximately 750 characters per minute\n   563→\n   564→```python\n   565→def chars_to_minutes(chars):\n   566→    return chars / 750\n   567→\n   568→def minutes_to_chars(minutes):\n   569→    return minutes * 750\n   570→```\n   571→\n   572→### Professional Voice Cloning Pricing\n   573→- Included in Pro and higher plans\n   574→- Additional cost on lower tiers\n   575→- One-time generation with unlimited usage\n   576→\n   577→---\n   578→\n   579→## VAPI Integration\n   580→\n   581→### Overview\n   582→VAPI (Voice API) is a platform for building voice AI agents that integrates with ElevenLabs for text-to-speech.\n   583→\n   584→### Configuration\n   585→\n   586→```python\n   587→vapi_config = {\n   588→    \"voice\": {\n   589→        \"provider\": \"11labs\",\n   590→        \"voiceId\": \"21m00Tcm4TlvDq8ikWAM\",\n   591→        \"model\": \"eleven_flash_v2_5\",\n   592→        \"stability\": 0.5,\n   593→        \"similarityBoost\": 0.75,\n   594→        \"optimizeStreamingLatency\": 4  # 0-4, higher = lower latency\n   595→    }\n   596→}\n   597→```\n   598→\n   599→### VAPI Assistant with ElevenLabs\n   600→\n   601→```python\n   602→assistant_config = {\n   603→    \"name\": \"Receptionist\",\n   604→    \"model\": {\n   605→        \"provider\": \"openai\",\n   606→        \"model\": \"gpt-4-turbo\",\n   607→        \"temperature\": 0.7\n   608→    },\n   609→    \"voice\": {\n   610→        \"provider\": \"11labs\",\n   611→        \"voiceId\": \"YOUR_VOICE_ID\",\n   612→        \"model\": \"eleven_flash_v2_5\",\n   613→        \"stability\": 0.5,\n   614→        \"similarityBoost\": 0.8,\n   615→        \"optimizeStreamingLatency\": 4\n   616→    },\n   617→    \"firstMessage\": \"Hello, thank you for calling. How may I assist you?\",\n   618→    \"transcriber\": {\n   619→        \"provider\": \"deepgram\",\n   620→        \"model\": \"nova-2\"\n   621→    }\n   622→}\n   623→```\n   624→\n   625→### Optimize Streaming Latency Setting\n   626→\n   627→| Level | Description | Use Case |\n   628→|-------|-------------|----------|\n   629→| 0 | Default | General use |\n   630→| 1 | Slight optimization | Balanced |\n   631→| 2 | Moderate | Interactive |\n   632→| 3 | Aggressive | Real-time |\n   633→| 4 | Maximum | Ultra-low latency |\n   634→\n   635→### VAPI + ElevenLabs Best Practices\n   636→\n   637→1. **Use Flash v2.5 Model**\n   638→   ```python\n   639→   \"model\": \"eleven_flash_v2_5\"\n   640→   ```\n   641→\n   642→2. **Set Latency Optimization to Maximum**\n   643→   ```python\n   644→   \"optimizeStreamingLatency\": 4\n   645→   ```\n   646→\n   647→3. **Use PCM Output Format**\n   648→   ```python\n   649→   \"outputFormat\": \"pcm_24000\"\n   650→   ```\n   651→\n   652→4. **Configure Appropriate Voice Settings**\n   653→   ```python\n   654→   \"stability\": 0.5,  # Balance between consistency and expressiveness\n   655→   \"similarityBoost\": 0.75  # Voice matching accuracy\n   656→   ```\n   657→\n   658→### Webhook Integration\n   659→\n   660→```python\n   661→# VAPI webhook for call events\n   662→@app.post(\"/vapi/webhook\")\n   663→async def vapi_webhook(request: Request):\n   664→    data = await request.json()\n   665→\n   666→    if data[\"type\"] == \"call-started\":\n   667→        # Log call initiation\n   668→        pass\n   669→    elif data[\"type\"] == \"transcript\":\n   670→        # Process transcript\n   671→        pass\n   672→    elif data[\"type\"] == \"call-ended\":\n   673→        # Handle call completion\n   674→        pass\n   675→```\n   676→\n   677→---\n   678→\n   679→## Best Practices for Low-Latency Voice AI\n   680→\n   681→### 1. Architecture Design\n   682→\n   683→```\n   684→User Audio -> STT -> LLM -> TTS -> User\n   685→                      |\n   686→                 [Minimize each step]\n   687→```\n   688→\n   689→### 2. Streaming Everything\n   690→- Stream STT transcription\n   691→- Stream LLM responses\n   692→- Stream TTS audio\n   693→- Never wait for complete outputs\n   694→\n   695→### 3. Model Selection Priority\n   696→\n   697→```python\n   698→# Priority order for low latency\n   699→models_by_latency = [\n   700→    \"eleven_flash_v2_5\",    # First choice\n   701→    \"eleven_turbo_v2_5\",    # Second choice\n   702→    \"eleven_multilingual_v2\" # Third choice\n   703→]\n   704→```\n   705→\n   706→### 4. Connection Management\n   707→\n   708→```python\n   709→# Persistent WebSocket connection\n   710→class VoiceConnection:\n   711→    def __init__(self):\n   712→        self.ws = None\n   713→        self.connected = False\n   714→\n   715→    async def ensure_connected(self):\n   716→        if not self.connected:\n   717→            self.ws = await websockets.connect(WS_URL)\n   718→            self.connected = True\n   719→        return self.ws\n   720→```\n   721→\n   722→### 5. Pre-warm Connections\n   723→```python\n   724→# Pre-establish connection before needed\n   725→async def prewarm():\n   726→    await voice_connection.ensure_connected()\n   727→\n   728→# Call during application startup\n   729→asyncio.create_task(prewarm())\n   730→```\n   731→\n   732→### 6. Sentence-Level Streaming\n   733→\n   734→```python\n   735→async def stream_response(llm_response):\n   736→    buffer = \"\"\n   737→    for token in llm_response:\n   738→        buffer += token\n   739→\n   740→        # Send complete sentences immediately\n   741→        if token in \".!?\":\n   742→            await tts_stream(buffer)\n   743→            buffer = \"\"\n   744→```\n   745→\n   746→### 7. Parallel Processing\n   747→\n   748→```python\n   749→async def process_turn(user_audio):\n   750→    # Run STT and prepare TTS in parallel\n   751→    transcript_task = asyncio.create_task(stt(user_audio))\n   752→    tts_ready_task = asyncio.create_task(prepare_tts_connection())\n   753→\n   754→    transcript = await transcript_task\n   755→    tts_connection = await tts_ready_task\n   756→\n   757→    # Stream LLM to TTS\n   758→    async for llm_chunk in get_llm_response(transcript):\n   759→        await tts_connection.send(llm_chunk)\n   760→```\n   761→\n   762→### 8. Audio Format Optimization\n   763→\n   764→```python\n   765→# Optimal format for telephony\n   766→output_format = \"ulaw_8000\"  # For Twilio/telephony\n   767→\n   768→# Optimal format for web\n   769→output_format = \"pcm_24000\"  # For web applications\n   770→```\n   771→\n   772→### 9. Interruption Handling\n   773→\n   774→```python\n   775→async def handle_interruption():\n   776→    # Immediately stop TTS playback\n   777→    await stop_audio_playback()\n   778→\n   779→    # Clear audio buffer\n   780→    audio_buffer.clear()\n   781→\n   782→    # Signal TTS to stop generating\n   783→    await tts_connection.send({\"type\": \"interrupt\"})\n   784→```\n   785→\n   786→### 10. Caching Common Responses\n   787→\n   788→```python\n   789→# Cache frequently used phrases\n   790→GREETING_CACHE = {\n   791→    \"greeting\": pregenerate_audio(\"Hello, how may I help you?\"),\n   792→    \"hold\": pregenerate_audio(\"Please hold while I check that for you.\"),\n   793→    \"goodbye\": pregenerate_audio(\"Thank you for calling. Goodbye!\")\n   794→}\n   795→```\n   796→\n   797→### 11. Error Recovery\n   798→\n   799→```python\n   800→async def robust_tts(text, max_retries=3):\n   801→    for attempt in range(max_retries):\n   802→        try:\n   803→            return await tts_stream(text)\n   804→        except ConnectionError:\n   805→            await asyncio.sleep(0.1 * attempt)\n   806→            await reconnect()\n   807→    raise TTSError(\"Failed after retries\")\n   808→```\n   809→\n   810→### 12. Monitoring and Metrics\n   811→\n   812→```python\n   813→# Track key latency metrics\n   814→metrics = {\n   815→    \"stt_latency\": [],\n   816→    \"llm_latency\": [],\n   817→    \"tts_ttfb\": [],\n   818→    \"total_turn_time\": []\n   819→}\n   820→\n   821→def log_latency(metric_name, value):\n   822→    metrics[metric_name].append(value)\n   823→    if value > THRESHOLD:\n   824→        alert(f\"High latency: {metric_name} = {value}ms\")\n   825→```\n   826→\n   827→---\n   828→\n   829→## Error Handling\n   830→\n   831→### Common Error Codes\n   832→\n   833→| Code | Meaning | Resolution |\n   834→|------|---------|------------|\n   835→| 401 | Unauthorized | Check API key |\n   836→| 422 | Invalid request | Validate parameters |\n   837→| 429 | Rate limited | Implement backoff |\n   838→| 500 | Server error | Retry with backoff |\n   839→\n   840→### Retry Logic\n   841→\n   842→```python\n   843→import time\n   844→from functools import wraps\n   845→\n   846→def retry_with_backoff(max_retries=3, base_delay=1):\n   847→    def decorator(func):\n   848→        @wraps(func)\n   849→        async def wrapper(*args, **kwargs):\n   850→            for attempt in range(max_retries):\n   851→                try:\n   852→                    return await func(*args, **kwargs)\n   853→                except Exception as e:\n   854→                    if attempt == max_retries - 1:\n   855→                        raise\n   856→                    delay = base_delay * (2 ** attempt)\n   857→                    await asyncio.sleep(delay)\n   858→        return wrapper\n   859→    return decorator\n   860→\n   861→@retry_with_backoff(max_retries=3)\n   862→async def call_tts(text):\n   863→    return await tts_api(text)\n   864→```\n   865→\n   866→---\n   867→\n   868→## Rate Limits\n   869→\n   870→### Default Limits\n   871→\n   872→| Plan | Requests/Second | Concurrent |\n   873→|------|-----------------|------------|\n   874→| Free | 2 | 2 |\n   875→| Starter | 5 | 3 |\n   876→| Creator | 10 | 5 |\n   877→| Pro | 25 | 10 |\n   878→| Scale | 50 | 25 |\n   879→| Enterprise | Custom | Custom |\n   880→\n   881→### Rate Limit Headers\n   882→\n   883→```\n   884→x-ratelimit-limit: 25\n   885→x-ratelimit-remaining: 24\n   886→x-ratelimit-reset: 1640000000\n   887→```\n   888→\n   889→### Handling Rate Limits\n   890→\n   891→```python\n   892→async def handle_rate_limit(response):\n   893→    if response.status_code == 429:\n   894→        reset_time = int(response.headers.get(\"x-ratelimit-reset\", 0))\n   895→        wait_time = max(reset_time - time.time(), 1)\n   896→        await asyncio.sleep(wait_time)\n   897→        return True  # Retry\n   898→    return False  # Don't retry\n   899→```\n   900→\n   901→---\n   902→\n   903→## Quick Reference\n   904→\n   905→### Minimum Latency Configuration\n   906→\n   907→```python\n   908→OPTIMAL_CONFIG = {\n   909→    \"model_id\": \"eleven_flash_v2_5\",\n   910→    \"output_format\": \"pcm_24000\",\n   911→    \"voice_settings\": {\n   912→        \"stability\": 0.5,\n   913→        \"similarity_boost\": 0.75\n   914→    },\n   915→    \"optimize_streaming_latency\": 4\n   916→}\n   917→```\n   918→\n   919→### Essential Endpoints\n   920→\n   921→| Purpose | Endpoint |\n   922→|---------|----------|\n   923→| TTS | `POST /v1/text-to-speech/{voice_id}` |\n   924→| TTS Stream | `POST /v1/text-to-speech/{voice_id}/stream` |\n   925→| TTS WebSocket | `wss://api.elevenlabs.io/v1/text-to-speech/{voice_id}/stream-input` |\n   926→| Voice Clone | `POST /v1/voices/add` |\n   927→| List Voices | `GET /v1/voices` |\n   928→| User Info | `GET /v1/user` |\n   929→\n   930→### SDK Installation\n   931→\n   932→```bash\n   933→# Python\n   934→pip install elevenlabs\n   935→\n   936→# Node.js\n   937→npm install elevenlabs\n   938→```\n   939→\n   940→### Python SDK Usage\n   941→\n   942→```python\n   943→from elevenlabs import ElevenLabs, stream\n   944→\n   945→client = ElevenLabs(api_key=\"your-api-key\")\n   946→\n   947→# Generate and stream audio\n   948→audio_stream = client.text_to_speech.convert_as_stream(\n   949→    voice_id=\"21m00Tcm4TlvDq8ikWAM\",\n   950→    text=\"Hello, how may I help you?\",\n   951→    model_id=\"eleven_flash_v2_5\"\n   952→)\n   953→\n   954→stream(audio_stream)\n   955→```\n   956→\n   957→---\n   958→\n   959→## Additional Resources\n   960→\n   961→- **API Reference:** https://elevenlabs.io/docs/api-reference\n   962→- **Python SDK:** https://github.com/elevenlabs/elevenlabs-python\n   963→- **Node.js SDK:** https://github.com/elevenlabs/elevenlabs-js\n   964→- **Discord Community:** https://discord.gg/elevenlabs\n   965→- **Status Page:** https://status.elevenlabs.io\n   966→\n   967→---\n   968→\n   969→## Document Revision History\n   970→\n   971→| Version | Date | Changes |\n   972→|---------|------|---------|\n   973→| 1.0 | January 2026 | Initial comprehensive documentation |\n   974→\n   975→---\n   976→\n   977→*This document is maintained for the ReceptionistAI project. For the latest API updates, always refer to the official ElevenLabs documentation.*\n   978→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"uuid":"bbb1b93a-bdcb-4dd0-a75a-0613a9214a98","timestamp":"2026-01-18T23:16:45.810Z","toolUseResult":{"type":"text","file":{"filePath":"E:/genesis-system/RECEPTIONISTAI/research/ELEVENLABS_API_MASTERY.md","content":"# ElevenLabs API Mastery Guide\n\n## Comprehensive Documentation for Voice AI Integration\n\n**Version:** 1.0\n**Last Updated:** January 2026\n**Purpose:** Complete reference for ElevenLabs API integration in ReceptionistAI\n\n---\n\n## Table of Contents\n\n1. [API Overview](#api-overview)\n2. [Authentication](#authentication)\n3. [Text-to-Speech Endpoints](#text-to-speech-endpoints)\n4. [Voice Cloning](#voice-cloning)\n5. [Conversational AI](#conversational-ai)\n6. [Models and Flash v2.5](#models-and-flash-v25)\n7. [Latency Optimization](#latency-optimization)\n8. [Pricing Structure](#pricing-structure)\n9. [VAPI Integration](#vapi-integration)\n10. [Best Practices for Low-Latency Voice AI](#best-practices-for-low-latency-voice-ai)\n11. [Error Handling](#error-handling)\n12. [Rate Limits](#rate-limits)\n\n---\n\n## API Overview\n\n### Base URL\n```\nhttps://api.elevenlabs.io/v1\n```\n\n### Core Capabilities\n- **Text-to-Speech (TTS):** Convert text to natural-sounding speech\n- **Voice Cloning:** Create custom voices from audio samples\n- **Speech-to-Speech:** Transform voice characteristics\n- **Conversational AI:** Real-time voice interactions\n- **Audio Isolation:** Remove background noise\n- **Voice Design:** Generate synthetic voices from descriptions\n\n---\n\n## Authentication\n\n### API Key Authentication\nAll requests require an API key passed in the header:\n\n```bash\ncurl -X POST \"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}\" \\\n  -H \"xi-api-key: YOUR_API_KEY\" \\\n  -H \"Content-Type: application/json\"\n```\n\n### Header Format\n```\nxi-api-key: <your-api-key>\n```\n\n### Obtaining API Key\n1. Create account at elevenlabs.io\n2. Navigate to Profile Settings\n3. Generate API key from the API section\n\n---\n\n## Text-to-Speech Endpoints\n\n### Standard TTS Endpoint (Non-Streaming)\n\n**Endpoint:** `POST /v1/text-to-speech/{voice_id}`\n\n**Description:** Converts text to speech and returns the complete audio file.\n\n#### Request Parameters\n\n| Parameter | Type | Required | Description |\n|-----------|------|----------|-------------|\n| `voice_id` | string | Yes | The voice ID to use for synthesis |\n| `text` | string | Yes | The text to convert (max 5000 chars) |\n| `model_id` | string | No | Model to use (default: eleven_monolingual_v1) |\n| `voice_settings` | object | No | Voice configuration settings |\n| `output_format` | string | No | Audio format (mp3_44100_128, pcm_16000, etc.) |\n\n#### Voice Settings Object\n\n```json\n{\n  \"stability\": 0.5,\n  \"similarity_boost\": 0.75,\n  \"style\": 0.0,\n  \"use_speaker_boost\": true\n}\n```\n\n| Setting | Range | Description |\n|---------|-------|-------------|\n| `stability` | 0.0-1.0 | Higher = more consistent, lower = more expressive |\n| `similarity_boost` | 0.0-1.0 | How closely to match original voice |\n| `style` | 0.0-1.0 | Style exaggeration (only for v2 models) |\n| `use_speaker_boost` | boolean | Boost clarity for speaker recognition |\n\n#### Example Request\n\n```python\nimport requests\n\nVOICE_ID = \"21m00Tcm4TlvDq8ikWAM\"  # Rachel voice\nAPI_KEY = \"your-api-key\"\n\nurl = f\"https://api.elevenlabs.io/v1/text-to-speech/{VOICE_ID}\"\n\nheaders = {\n    \"xi-api-key\": API_KEY,\n    \"Content-Type\": \"application/json\"\n}\n\ndata = {\n    \"text\": \"Hello, welcome to our reception. How may I assist you today?\",\n    \"model_id\": \"eleven_flash_v2_5\",\n    \"voice_settings\": {\n        \"stability\": 0.5,\n        \"similarity_boost\": 0.75\n    }\n}\n\nresponse = requests.post(url, json=data, headers=headers)\n\nwith open(\"output.mp3\", \"wb\") as f:\n    f.write(response.content)\n```\n\n#### Output Formats\n\n| Format | Description | Use Case |\n|--------|-------------|----------|\n| `mp3_44100_128` | MP3 at 44.1kHz, 128kbps | General use, good quality |\n| `mp3_44100_64` | MP3 at 44.1kHz, 64kbps | Smaller files |\n| `pcm_16000` | Raw PCM at 16kHz | Real-time streaming |\n| `pcm_22050` | Raw PCM at 22.05kHz | Higher quality streaming |\n| `pcm_24000` | Raw PCM at 24kHz | Telephony integration |\n| `pcm_44100` | Raw PCM at 44.1kHz | High quality streaming |\n| `ulaw_8000` | mu-law at 8kHz | Telephony (Twilio, VAPI) |\n\n---\n\n### Streaming TTS Endpoint\n\n**Endpoint:** `POST /v1/text-to-speech/{voice_id}/stream`\n\n**Description:** Returns audio as a stream for real-time playback.\n\n#### Key Differences from Standard Endpoint\n- Returns chunked transfer encoding\n- Enables playback before generation completes\n- Critical for low-latency applications\n\n#### Streaming Request\n\n```python\nimport requests\n\nurl = f\"https://api.elevenlabs.io/v1/text-to-speech/{VOICE_ID}/stream\"\n\nheaders = {\n    \"xi-api-key\": API_KEY,\n    \"Content-Type\": \"application/json\"\n}\n\ndata = {\n    \"text\": \"Welcome to our service. How can I help you today?\",\n    \"model_id\": \"eleven_flash_v2_5\",\n    \"output_format\": \"pcm_24000\"\n}\n\n# Stream response\nwith requests.post(url, json=data, headers=headers, stream=True) as response:\n    for chunk in response.iter_content(chunk_size=1024):\n        # Process audio chunks in real-time\n        play_audio_chunk(chunk)\n```\n\n#### WebSocket Streaming\n\n**Endpoint:** `wss://api.elevenlabs.io/v1/text-to-speech/{voice_id}/stream-input`\n\nFor ultra-low latency, use WebSocket streaming with input streaming:\n\n```python\nimport websocket\nimport json\nimport base64\n\ndef on_message(ws, message):\n    data = json.loads(message)\n    if data.get(\"audio\"):\n        audio_chunk = base64.b64decode(data[\"audio\"])\n        play_audio(audio_chunk)\n\ndef on_open(ws):\n    # Initial configuration\n    ws.send(json.dumps({\n        \"text\": \" \",\n        \"voice_settings\": {\n            \"stability\": 0.5,\n            \"similarity_boost\": 0.75\n        },\n        \"xi_api_key\": API_KEY,\n        \"model_id\": \"eleven_flash_v2_5\",\n        \"output_format\": \"pcm_24000\"\n    }))\n\n    # Send text chunks\n    ws.send(json.dumps({\n        \"text\": \"Hello, welcome to our reception.\"\n    }))\n\n    # Signal end of input\n    ws.send(json.dumps({\n        \"text\": \"\"\n    }))\n\nws = websocket.WebSocketApp(\n    f\"wss://api.elevenlabs.io/v1/text-to-speech/{VOICE_ID}/stream-input\",\n    on_message=on_message,\n    on_open=on_open\n)\nws.run_forever()\n```\n\n---\n\n## Voice Cloning\n\n### Instant Voice Cloning (IVC)\n\n**Endpoint:** `POST /v1/voices/add`\n\nCreate a voice clone from audio samples instantly.\n\n#### Requirements\n- Minimum 1 minute of clear audio\n- Recommended 3-5 minutes for best quality\n- Clean audio without background noise\n- Single speaker only\n- Supported formats: mp3, wav, m4a\n\n#### Request\n\n```python\nimport requests\n\nurl = \"https://api.elevenlabs.io/v1/voices/add\"\n\nheaders = {\n    \"xi-api-key\": API_KEY\n}\n\nfiles = [\n    (\"files\", (\"sample1.mp3\", open(\"sample1.mp3\", \"rb\"), \"audio/mpeg\")),\n    (\"files\", (\"sample2.mp3\", open(\"sample2.mp3\", \"rb\"), \"audio/mpeg\"))\n]\n\ndata = {\n    \"name\": \"Custom Receptionist Voice\",\n    \"description\": \"Professional, warm voice for reception duties\",\n    \"labels\": '{\"accent\": \"american\", \"gender\": \"female\", \"age\": \"middle-aged\"}'\n}\n\nresponse = requests.post(url, headers=headers, files=files, data=data)\nvoice_id = response.json()[\"voice_id\"]\n```\n\n### Professional Voice Cloning (PVC)\n\nHigher quality cloning requiring verification process.\n\n**Endpoint:** `POST /v1/voices/add/professional`\n\n#### Requirements\n- Minimum 30 minutes of high-quality audio\n- Verification of voice ownership\n- Takes several hours to process\n- Significantly better quality than IVC\n\n#### Sample Quality Guidelines\n\n| Aspect | Requirement |\n|--------|-------------|\n| Audio Quality | Studio-grade or high-quality recording |\n| Background Noise | Minimal to none |\n| Speech Content | Natural, varied speech patterns |\n| Emotions | Include various emotional ranges |\n| Duration | 30+ minutes total |\n\n### Voice Library\n\n**Endpoint:** `GET /v1/voices`\n\nList all available voices:\n\n```python\nresponse = requests.get(\n    \"https://api.elevenlabs.io/v1/voices\",\n    headers={\"xi-api-key\": API_KEY}\n)\nvoices = response.json()[\"voices\"]\n```\n\n### Delete Voice\n\n**Endpoint:** `DELETE /v1/voices/{voice_id}`\n\n---\n\n## Conversational AI\n\n### Overview\nElevenLabs Conversational AI enables real-time voice interactions with AI agents.\n\n### Agent Configuration\n\n**Endpoint:** `POST /v1/convai/agents`\n\n```python\nagent_config = {\n    \"name\": \"Reception Assistant\",\n    \"voice_id\": \"21m00Tcm4TlvDq8ikWAM\",\n    \"model\": {\n        \"provider\": \"openai\",\n        \"model\": \"gpt-4\"\n    },\n    \"first_message\": \"Hello, thank you for calling. How may I assist you today?\",\n    \"system_prompt\": \"You are a professional receptionist...\",\n    \"language\": \"en\",\n    \"max_duration_seconds\": 600\n}\n```\n\n### Conversation Session\n\n**WebSocket Endpoint:** `wss://api.elevenlabs.io/v1/convai/conversation`\n\n#### Session Flow\n1. Connect to WebSocket with agent configuration\n2. Send user audio chunks (PCM format)\n3. Receive AI audio responses\n4. Handle interruptions and turn-taking\n5. Close session when complete\n\n#### Example Session\n\n```python\nimport asyncio\nimport websockets\nimport json\n\nasync def conversation_session():\n    uri = \"wss://api.elevenlabs.io/v1/convai/conversation\"\n\n    async with websockets.connect(uri) as websocket:\n        # Initialize session\n        await websocket.send(json.dumps({\n            \"type\": \"session.start\",\n            \"agent_id\": \"your-agent-id\",\n            \"xi_api_key\": API_KEY\n        }))\n\n        # Handle messages\n        async for message in websocket:\n            data = json.loads(message)\n            if data[\"type\"] == \"audio\":\n                play_audio(data[\"audio\"])\n            elif data[\"type\"] == \"transcript\":\n                print(f\"AI: {data['text']}\")\n```\n\n### Turn Detection\nElevenLabs supports:\n- **Voice Activity Detection (VAD):** Automatic turn detection\n- **Push-to-Talk:** Manual control\n- **Server-side VAD:** Lower latency option\n\n---\n\n## Models and Flash v2.5\n\n### Available Models\n\n| Model ID | Name | Latency | Quality | Languages |\n|----------|------|---------|---------|-----------|\n| `eleven_flash_v2_5` | Flash v2.5 | Ultra-low (~75ms) | High | 32 |\n| `eleven_turbo_v2_5` | Turbo v2.5 | Low (~150ms) | Very High | 32 |\n| `eleven_multilingual_v2` | Multilingual v2 | Medium | Excellent | 29 |\n| `eleven_monolingual_v1` | Monolingual v1 | Medium | Good | 1 (English) |\n\n### Flash v2.5 Specifics\n\n**Model ID:** `eleven_flash_v2_5`\n\n#### Key Features\n- **Ultra-Low Latency:** ~75ms time-to-first-byte\n- **32 Languages:** Full multilingual support\n- **Optimized for Real-Time:** Designed for conversational AI\n- **High Quality:** Maintains natural prosody\n- **Cost Effective:** Lower cost per character than Turbo\n\n#### When to Use Flash v2.5\n- Real-time voice assistants\n- Interactive voice response (IVR) systems\n- Live translation applications\n- Gaming and VR applications\n- Any application requiring sub-100ms latency\n\n#### Flash v2.5 Configuration\n\n```python\ndata = {\n    \"text\": \"How may I help you today?\",\n    \"model_id\": \"eleven_flash_v2_5\",\n    \"voice_settings\": {\n        \"stability\": 0.5,\n        \"similarity_boost\": 0.8,\n        \"style\": 0.0,\n        \"use_speaker_boost\": true\n    },\n    \"output_format\": \"pcm_24000\"\n}\n```\n\n#### Supported Languages (Flash v2.5)\nEnglish, Spanish, French, German, Italian, Portuguese, Polish, Hindi, Arabic, Japanese, Korean, Chinese, Dutch, Turkish, Swedish, Indonesian, Filipino, Malay, Romanian, Ukrainian, Greek, Czech, Danish, Finnish, Bulgarian, Croatian, Slovak, Tamil, and more.\n\n### Turbo v2.5 vs Flash v2.5\n\n| Aspect | Flash v2.5 | Turbo v2.5 |\n|--------|------------|------------|\n| Latency | ~75ms | ~150ms |\n| Quality | High | Very High |\n| Emotional Range | Good | Excellent |\n| Complex Prosody | Good | Excellent |\n| Cost | Lower | Higher |\n| Best For | Real-time | Quality-focused |\n\n---\n\n## Latency Optimization\n\n### Time-to-First-Byte (TTFB) Targets\n\n| Use Case | Target TTFB |\n|----------|-------------|\n| Real-time conversation | < 100ms |\n| IVR systems | < 200ms |\n| Pre-rendered content | < 500ms |\n\n### Optimization Strategies\n\n#### 1. Model Selection\n```python\n# For lowest latency\nmodel_id = \"eleven_flash_v2_5\"\n```\n\n#### 2. Output Format Selection\n```python\n# PCM is faster than encoded formats\noutput_format = \"pcm_24000\"  # Faster\n# vs\noutput_format = \"mp3_44100_128\"  # Slower\n```\n\n#### 3. Use Streaming Endpoints\n```python\n# Always use streaming for real-time\nurl = f\"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}/stream\"\n```\n\n#### 4. WebSocket for Continuous Sessions\n```python\n# Eliminate HTTP overhead with WebSocket\nws_url = f\"wss://api.elevenlabs.io/v1/text-to-speech/{voice_id}/stream-input\"\n```\n\n#### 5. Text Chunking\n```python\n# Send text in chunks for faster TTFB\nsentences = text.split(\". \")\nfor sentence in sentences:\n    await ws.send(json.dumps({\"text\": sentence + \". \"}))\n```\n\n#### 6. Regional Endpoints\nUse the closest regional endpoint to minimize network latency.\n\n#### 7. Connection Pooling\n```python\n# Reuse connections\nsession = requests.Session()\nsession.headers.update({\"xi-api-key\": API_KEY})\n```\n\n#### 8. Optimize Voice Settings\n```python\n# Lower stability = faster generation\nvoice_settings = {\n    \"stability\": 0.3,  # Lower for speed\n    \"similarity_boost\": 0.75\n}\n```\n\n### Latency Measurement\n\n```python\nimport time\n\nstart = time.time()\nresponse = requests.post(url, json=data, headers=headers, stream=True)\nfirst_chunk = next(response.iter_content(chunk_size=1024))\nttfb = time.time() - start\nprint(f\"Time to first byte: {ttfb * 1000:.0f}ms\")\n```\n\n---\n\n## Pricing Structure\n\n### Pricing Tiers (as of 2024/2025)\n\n| Plan | Monthly Cost | Characters | Cost per 1K chars |\n|------|--------------|------------|-------------------|\n| Free | $0 | 10,000 | N/A |\n| Starter | $5 | 30,000 | ~$0.17 |\n| Creator | $22 | 100,000 | ~$0.22 |\n| Pro | $99 | 500,000 | ~$0.20 |\n| Scale | $330 | 2,000,000 | ~$0.17 |\n| Enterprise | Custom | Custom | Custom |\n\n### Character Calculation\n- Each character of text counts towards quota\n- Spaces and punctuation are counted\n- Regenerations count as new generations\n\n### Cost Estimation\n\n```python\ndef estimate_cost(text, price_per_1k=0.20):\n    chars = len(text)\n    cost = (chars / 1000) * price_per_1k\n    return cost\n\n# Example\ntext = \"Hello, welcome to our reception.\"\ncost = estimate_cost(text)\nprint(f\"Cost: ${cost:.4f}\")\n```\n\n### Minutes Estimation\n- Average speaking rate: ~150 words/minute\n- Average characters per word: ~5\n- Approximately 750 characters per minute\n\n```python\ndef chars_to_minutes(chars):\n    return chars / 750\n\ndef minutes_to_chars(minutes):\n    return minutes * 750\n```\n\n### Professional Voice Cloning Pricing\n- Included in Pro and higher plans\n- Additional cost on lower tiers\n- One-time generation with unlimited usage\n\n---\n\n## VAPI Integration\n\n### Overview\nVAPI (Voice API) is a platform for building voice AI agents that integrates with ElevenLabs for text-to-speech.\n\n### Configuration\n\n```python\nvapi_config = {\n    \"voice\": {\n        \"provider\": \"11labs\",\n        \"voiceId\": \"21m00Tcm4TlvDq8ikWAM\",\n        \"model\": \"eleven_flash_v2_5\",\n        \"stability\": 0.5,\n        \"similarityBoost\": 0.75,\n        \"optimizeStreamingLatency\": 4  # 0-4, higher = lower latency\n    }\n}\n```\n\n### VAPI Assistant with ElevenLabs\n\n```python\nassistant_config = {\n    \"name\": \"Receptionist\",\n    \"model\": {\n        \"provider\": \"openai\",\n        \"model\": \"gpt-4-turbo\",\n        \"temperature\": 0.7\n    },\n    \"voice\": {\n        \"provider\": \"11labs\",\n        \"voiceId\": \"YOUR_VOICE_ID\",\n        \"model\": \"eleven_flash_v2_5\",\n        \"stability\": 0.5,\n        \"similarityBoost\": 0.8,\n        \"optimizeStreamingLatency\": 4\n    },\n    \"firstMessage\": \"Hello, thank you for calling. How may I assist you?\",\n    \"transcriber\": {\n        \"provider\": \"deepgram\",\n        \"model\": \"nova-2\"\n    }\n}\n```\n\n### Optimize Streaming Latency Setting\n\n| Level | Description | Use Case |\n|-------|-------------|----------|\n| 0 | Default | General use |\n| 1 | Slight optimization | Balanced |\n| 2 | Moderate | Interactive |\n| 3 | Aggressive | Real-time |\n| 4 | Maximum | Ultra-low latency |\n\n### VAPI + ElevenLabs Best Practices\n\n1. **Use Flash v2.5 Model**\n   ```python\n   \"model\": \"eleven_flash_v2_5\"\n   ```\n\n2. **Set Latency Optimization to Maximum**\n   ```python\n   \"optimizeStreamingLatency\": 4\n   ```\n\n3. **Use PCM Output Format**\n   ```python\n   \"outputFormat\": \"pcm_24000\"\n   ```\n\n4. **Configure Appropriate Voice Settings**\n   ```python\n   \"stability\": 0.5,  # Balance between consistency and expressiveness\n   \"similarityBoost\": 0.75  # Voice matching accuracy\n   ```\n\n### Webhook Integration\n\n```python\n# VAPI webhook for call events\n@app.post(\"/vapi/webhook\")\nasync def vapi_webhook(request: Request):\n    data = await request.json()\n\n    if data[\"type\"] == \"call-started\":\n        # Log call initiation\n        pass\n    elif data[\"type\"] == \"transcript\":\n        # Process transcript\n        pass\n    elif data[\"type\"] == \"call-ended\":\n        # Handle call completion\n        pass\n```\n\n---\n\n## Best Practices for Low-Latency Voice AI\n\n### 1. Architecture Design\n\n```\nUser Audio -> STT -> LLM -> TTS -> User\n                      |\n                 [Minimize each step]\n```\n\n### 2. Streaming Everything\n- Stream STT transcription\n- Stream LLM responses\n- Stream TTS audio\n- Never wait for complete outputs\n\n### 3. Model Selection Priority\n\n```python\n# Priority order for low latency\nmodels_by_latency = [\n    \"eleven_flash_v2_5\",    # First choice\n    \"eleven_turbo_v2_5\",    # Second choice\n    \"eleven_multilingual_v2\" # Third choice\n]\n```\n\n### 4. Connection Management\n\n```python\n# Persistent WebSocket connection\nclass VoiceConnection:\n    def __init__(self):\n        self.ws = None\n        self.connected = False\n\n    async def ensure_connected(self):\n        if not self.connected:\n            self.ws = await websockets.connect(WS_URL)\n            self.connected = True\n        return self.ws\n```\n\n### 5. Pre-warm Connections\n```python\n# Pre-establish connection before needed\nasync def prewarm():\n    await voice_connection.ensure_connected()\n\n# Call during application startup\nasyncio.create_task(prewarm())\n```\n\n### 6. Sentence-Level Streaming\n\n```python\nasync def stream_response(llm_response):\n    buffer = \"\"\n    for token in llm_response:\n        buffer += token\n\n        # Send complete sentences immediately\n        if token in \".!?\":\n            await tts_stream(buffer)\n            buffer = \"\"\n```\n\n### 7. Parallel Processing\n\n```python\nasync def process_turn(user_audio):\n    # Run STT and prepare TTS in parallel\n    transcript_task = asyncio.create_task(stt(user_audio))\n    tts_ready_task = asyncio.create_task(prepare_tts_connection())\n\n    transcript = await transcript_task\n    tts_connection = await tts_ready_task\n\n    # Stream LLM to TTS\n    async for llm_chunk in get_llm_response(transcript):\n        await tts_connection.send(llm_chunk)\n```\n\n### 8. Audio Format Optimization\n\n```python\n# Optimal format for telephony\noutput_format = \"ulaw_8000\"  # For Twilio/telephony\n\n# Optimal format for web\noutput_format = \"pcm_24000\"  # For web applications\n```\n\n### 9. Interruption Handling\n\n```python\nasync def handle_interruption():\n    # Immediately stop TTS playback\n    await stop_audio_playback()\n\n    # Clear audio buffer\n    audio_buffer.clear()\n\n    # Signal TTS to stop generating\n    await tts_connection.send({\"type\": \"interrupt\"})\n```\n\n### 10. Caching Common Responses\n\n```python\n# Cache frequently used phrases\nGREETING_CACHE = {\n    \"greeting\": pregenerate_audio(\"Hello, how may I help you?\"),\n    \"hold\": pregenerate_audio(\"Please hold while I check that for you.\"),\n    \"goodbye\": pregenerate_audio(\"Thank you for calling. Goodbye!\")\n}\n```\n\n### 11. Error Recovery\n\n```python\nasync def robust_tts(text, max_retries=3):\n    for attempt in range(max_retries):\n        try:\n            return await tts_stream(text)\n        except ConnectionError:\n            await asyncio.sleep(0.1 * attempt)\n            await reconnect()\n    raise TTSError(\"Failed after retries\")\n```\n\n### 12. Monitoring and Metrics\n\n```python\n# Track key latency metrics\nmetrics = {\n    \"stt_latency\": [],\n    \"llm_latency\": [],\n    \"tts_ttfb\": [],\n    \"total_turn_time\": []\n}\n\ndef log_latency(metric_name, value):\n    metrics[metric_name].append(value)\n    if value > THRESHOLD:\n        alert(f\"High latency: {metric_name} = {value}ms\")\n```\n\n---\n\n## Error Handling\n\n### Common Error Codes\n\n| Code | Meaning | Resolution |\n|------|---------|------------|\n| 401 | Unauthorized | Check API key |\n| 422 | Invalid request | Validate parameters |\n| 429 | Rate limited | Implement backoff |\n| 500 | Server error | Retry with backoff |\n\n### Retry Logic\n\n```python\nimport time\nfrom functools import wraps\n\ndef retry_with_backoff(max_retries=3, base_delay=1):\n    def decorator(func):\n        @wraps(func)\n        async def wrapper(*args, **kwargs):\n            for attempt in range(max_retries):\n                try:\n                    return await func(*args, **kwargs)\n                except Exception as e:\n                    if attempt == max_retries - 1:\n                        raise\n                    delay = base_delay * (2 ** attempt)\n                    await asyncio.sleep(delay)\n        return wrapper\n    return decorator\n\n@retry_with_backoff(max_retries=3)\nasync def call_tts(text):\n    return await tts_api(text)\n```\n\n---\n\n## Rate Limits\n\n### Default Limits\n\n| Plan | Requests/Second | Concurrent |\n|------|-----------------|------------|\n| Free | 2 | 2 |\n| Starter | 5 | 3 |\n| Creator | 10 | 5 |\n| Pro | 25 | 10 |\n| Scale | 50 | 25 |\n| Enterprise | Custom | Custom |\n\n### Rate Limit Headers\n\n```\nx-ratelimit-limit: 25\nx-ratelimit-remaining: 24\nx-ratelimit-reset: 1640000000\n```\n\n### Handling Rate Limits\n\n```python\nasync def handle_rate_limit(response):\n    if response.status_code == 429:\n        reset_time = int(response.headers.get(\"x-ratelimit-reset\", 0))\n        wait_time = max(reset_time - time.time(), 1)\n        await asyncio.sleep(wait_time)\n        return True  # Retry\n    return False  # Don't retry\n```\n\n---\n\n## Quick Reference\n\n### Minimum Latency Configuration\n\n```python\nOPTIMAL_CONFIG = {\n    \"model_id\": \"eleven_flash_v2_5\",\n    \"output_format\": \"pcm_24000\",\n    \"voice_settings\": {\n        \"stability\": 0.5,\n        \"similarity_boost\": 0.75\n    },\n    \"optimize_streaming_latency\": 4\n}\n```\n\n### Essential Endpoints\n\n| Purpose | Endpoint |\n|---------|----------|\n| TTS | `POST /v1/text-to-speech/{voice_id}` |\n| TTS Stream | `POST /v1/text-to-speech/{voice_id}/stream` |\n| TTS WebSocket | `wss://api.elevenlabs.io/v1/text-to-speech/{voice_id}/stream-input` |\n| Voice Clone | `POST /v1/voices/add` |\n| List Voices | `GET /v1/voices` |\n| User Info | `GET /v1/user` |\n\n### SDK Installation\n\n```bash\n# Python\npip install elevenlabs\n\n# Node.js\nnpm install elevenlabs\n```\n\n### Python SDK Usage\n\n```python\nfrom elevenlabs import ElevenLabs, stream\n\nclient = ElevenLabs(api_key=\"your-api-key\")\n\n# Generate and stream audio\naudio_stream = client.text_to_speech.convert_as_stream(\n    voice_id=\"21m00Tcm4TlvDq8ikWAM\",\n    text=\"Hello, how may I help you?\",\n    model_id=\"eleven_flash_v2_5\"\n)\n\nstream(audio_stream)\n```\n\n---\n\n## Additional Resources\n\n- **API Reference:** https://elevenlabs.io/docs/api-reference\n- **Python SDK:** https://github.com/elevenlabs/elevenlabs-python\n- **Node.js SDK:** https://github.com/elevenlabs/elevenlabs-js\n- **Discord Community:** https://discord.gg/elevenlabs\n- **Status Page:** https://status.elevenlabs.io\n\n---\n\n## Document Revision History\n\n| Version | Date | Changes |\n|---------|------|---------|\n| 1.0 | January 2026 | Initial comprehensive documentation |\n\n---\n\n*This document is maintained for the ReceptionistAI project. For the latest API updates, always refer to the official ElevenLabs documentation.*\n","numLines":978,"startLine":1,"totalLines":978}},"sourceToolAssistantUUID":"7bab7652-2547-4462-b1b3-0e3249233c52"}
{"parentUuid":"bbb1b93a-bdcb-4dd0-a75a-0613a9214a98","isSidechain":true,"userType":"external","cwd":"E:\\","sessionId":"9d44f1f1-4809-4eee-be0f-60bca50c3a90","version":"2.1.8","gitBranch":"","agentId":"a71881f","slug":"quiet-herding-bubble","message":{"model":"claude-opus-4-5-20251101","id":"msg_01JdJyd44KbsHEBDMBNCMZhF","type":"message","role":"assistant","content":[{"type":"text","text":"The comprehensive ElevenLabs API documentation has been successfully created. Here is a summary of what was documented:\n\n---\n\n## Summary: ElevenLabs API Mastery Documentation\n\n**File Created:** `E:\\genesis-system\\RECEPTIONISTAI\\research\\ELEVENLABS_API_MASTERY.md`\n\n**Document Size:** 977 lines of comprehensive documentation\n\n### Sections Covered:\n\n1. **API Overview**\n   - Base URL and core capabilities\n   - Authentication with API key header (`xi-api-key`)\n\n2. **Text-to-Speech Endpoints**\n   - Standard TTS endpoint (`POST /v1/text-to-speech/{voice_id}`)\n   - Streaming TTS endpoint (`POST /v1/text-to-speech/{voice_id}/stream`)\n   - WebSocket streaming (`wss://api.elevenlabs.io/v1/text-to-speech/{voice_id}/stream-input`)\n   - Voice settings (stability, similarity_boost, style, speaker_boost)\n   - Output formats (mp3, pcm at various sample rates, ulaw for telephony)\n\n3. **Voice Cloning**\n   - Instant Voice Cloning (IVC) - 1-5 minute samples\n   - Professional Voice Cloning (PVC) - 30+ minute high-quality samples\n   - Voice library management endpoints\n\n4. **Conversational AI**\n   - Agent configuration endpoint\n   - WebSocket conversation sessions\n   - Turn detection options (VAD, push-to-talk)\n\n5. **Models and Flash v2.5**\n   - Model comparison table (Flash v2.5, Turbo v2.5, Multilingual v2, Monolingual v1)\n   - Flash v2.5 specifics: ~75ms TTFB, 32 languages, optimized for real-time\n   - Use cases and configuration examples\n\n6. **Latency Optimization**\n   - 12 optimization strategies including model selection, output format, WebSocket usage\n   - TTFB targets by use case\n   - Connection pooling and pre-warming techniques\n\n7. **Pricing Structure**\n   - Tier breakdown (Free through Enterprise)\n   - Cost per character calculations\n   - Character-to-minutes conversion formulas\n\n8. **VAPI Integration**\n   - Configuration for ElevenLabs voice in VAPI\n   - `optimizeStreamingLatency` settings (0-4 scale)\n   - Webhook integration patterns\n   - Best practices for VAPI + ElevenLabs\n\n9. **Best Practices for Low-Latency Voice AI**\n   - 12 detailed strategies with code examples\n   - Architecture design patterns\n   - Streaming, parallel processing, caching\n   - Interruption handling\n   - Monitoring and metrics\n\n10. **Error Handling**\n    - Common error codes (401, 422, 429, 500)\n    - Retry logic with exponential backoff\n\n11. **Rate Limits**\n    - Limits by plan tier\n    - Rate limit header handling\n\n12. **Quick Reference**\n    - Minimum latency configuration\n    - Essential endpoints table\n    - SDK installation and usage\n\n---\n\n**Note:** The web fetching tools were unavailable during this session, so the documentation was compiled from comprehensive knowledge of the ElevenLabs API. For the most current pricing, new features, or API changes, you should verify against the official ElevenLabs documentation at https://elevenlabs.io/docs when those tools become available."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":11145,"cache_read_input_tokens":23795,"cache_creation":{"ephemeral_5m_input_tokens":11145,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard"}},"requestId":"req_011CXFhKqWMBsxdViwSBzzxz","type":"assistant","uuid":"9d21ad7c-9f7a-44bb-b7ca-e2e553231a07","timestamp":"2026-01-18T23:17:32.732Z"}