> ## Documentation Index > Fetch the complete documentation index at: https://docs.plaisolutions.com/llms.txt > Use this file to discover all available pages before exploring further. # Usage > API endpoints for tracking and analyzing usage analytics across all PLai services ## Get Requests Usage Get aggregated request statistics for your project, showing API usage patterns over time. ### Query Parameters Page number for pagination (default: 1) Start date for usage data (ISO 8601 format, e.g., "2024-01-01") End date for usage data (ISO 8601 format, e.g., "2024-01-31") ### Response Array of daily request statistics Date in YYYY-MM-DD format Total number of API requests for that date ```bash cURL theme={null} curl --location --request GET 'https://api.plaisolutions.com/usage/requests?date_from=2024-01-01&date_to=2024-01-31' \ --header 'Authorization: Bearer YOUR_TOKEN' ``` ```javascript JavaScript theme={null} const response = await fetch('https://api.plaisolutions.com/usage/requests?date_from=2024-01-01&date_to=2024-01-31', { method: 'GET', headers: { 'Authorization': 'Bearer YOUR_TOKEN', 'Content-Type': 'application/json' } }); const requestsUsage = await response.json(); console.log(requestsUsage); ``` ```python Python theme={null} import requests url = "https://api.plaisolutions.com/usage/requests" headers = { "Authorization": "Bearer YOUR_TOKEN", "Content-Type": "application/json" } params = { "date_from": "2024-01-01", "date_to": "2024-01-31" } response = requests.get(url, headers=headers, params=params) requests_usage = response.json() print(requests_usage) ``` *** ## Get Threads Usage Get conversation thread statistics showing how users interact with your agents over time. ### Query Parameters Start date for usage data (ISO 8601 format) End date for usage data (ISO 8601 format) ### Response Array of daily thread statistics Date in YYYY-MM-DD format Number of new conversation threads created on that date ```bash cURL theme={null} curl --location --request GET 'https://api.plaisolutions.com/usage/threads?date_from=2024-01-01&date_to=2024-01-31' \ --header 'Authorization: Bearer YOUR_TOKEN' ``` ```javascript JavaScript theme={null} const response = await fetch('https://api.plaisolutions.com/usage/threads?date_from=2024-01-01&date_to=2024-01-31', { method: 'GET', headers: { 'Authorization': 'Bearer YOUR_TOKEN', 'Content-Type': 'application/json' } }); const threadsUsage = await response.json(); console.log(threadsUsage); ``` ```python Python theme={null} import requests url = "https://api.plaisolutions.com/usage/threads" headers = { "Authorization": "Bearer YOUR_TOKEN", "Content-Type": "application/json" } params = { "date_from": "2024-01-01", "date_to": "2024-01-31" } response = requests.get(url, headers=headers, params=params) threads_usage = response.json() print(threads_usage) ``` *** ## Get LLM Usage Get detailed statistics about language model usage including token consumption and costs. ### Query Parameters Start date for usage data (ISO 8601 format) End date for usage data (ISO 8601 format) ### Response Array of LLM usage statistics The specific language model used (e.g., "gpt-4", "claude-3-sonnet") Type of operation (e.g., "completion", "chat") Token consumption aggregation Total input tokens consumed Total output tokens generated ```bash cURL theme={null} curl --location --request GET 'https://api.plaisolutions.com/usage/llm?date_from=2024-01-01&date_to=2024-01-31' \ --header 'Authorization: Bearer YOUR_TOKEN' ``` ```javascript JavaScript theme={null} const response = await fetch('https://api.plaisolutions.com/usage/llm?date_from=2024-01-01&date_to=2024-01-31', { method: 'GET', headers: { 'Authorization': 'Bearer YOUR_TOKEN', 'Content-Type': 'application/json' } }); const llmUsage = await response.json(); console.log(llmUsage); ``` ```python Python theme={null} import requests url = "https://api.plaisolutions.com/usage/llm" headers = { "Authorization": "Bearer YOUR_TOKEN", "Content-Type": "application/json" } params = { "date_from": "2024-01-01", "date_to": "2024-01-31" } response = requests.get(url, headers=headers, params=params) llm_usage = response.json() print(llm_usage) ``` *** ## Get Rerank Usage Get statistics about vector reranking operations used to improve search result quality. ### Query Parameters Start date for usage data (ISO 8601 format) End date for usage data (ISO 8601 format) ### Response Array of reranking usage statistics The reranking model used The service provider (e.g., "cohere", "openai") Total search units consumed for reranking operations ```bash cURL theme={null} curl --location --request GET 'https://api.plaisolutions.com/usage/rerank?date_from=2024-01-01&date_to=2024-01-31' \ --header 'Authorization: Bearer YOUR_TOKEN' ``` ```javascript JavaScript theme={null} const response = await fetch('https://api.plaisolutions.com/usage/rerank?date_from=2024-01-01&date_to=2024-01-31', { method: 'GET', headers: { 'Authorization': 'Bearer YOUR_TOKEN', 'Content-Type': 'application/json' } }); const rerankUsage = await response.json(); console.log(rerankUsage); ``` ```python Python theme={null} import requests url = "https://api.plaisolutions.com/usage/rerank" headers = { "Authorization": "Bearer YOUR_TOKEN", "Content-Type": "application/json" } params = { "date_from": "2024-01-01", "date_to": "2024-01-31" } response = requests.get(url, headers=headers, params=params) rerank_usage = response.json() print(rerank_usage) ``` *** ## Get Embeddings Usage Get statistics about embedding generation for vector search and semantic operations. ### Query Parameters Start date for usage data (ISO 8601 format) End date for usage data (ISO 8601 format) ### Response Array of embedding usage statistics The embedding model used (e.g., "text-embedding-3-small", "text-embedding-ada-002") Type of embedding operation Total tokens processed for embedding generation ```bash cURL theme={null} curl --location --request GET 'https://api.plaisolutions.com/usage/embeddings?date_from=2024-01-01&date_to=2024-01-31' \ --header 'Authorization: Bearer YOUR_TOKEN' ``` ```javascript JavaScript theme={null} const response = await fetch('https://api.plaisolutions.com/usage/embeddings?date_from=2024-01-01&date_to=2024-01-31', { method: 'GET', headers: { 'Authorization': 'Bearer YOUR_TOKEN', 'Content-Type': 'application/json' } }); const embeddingsUsage = await response.json(); console.log(embeddingsUsage); ``` ```python Python theme={null} import requests url = "https://api.plaisolutions.com/usage/embeddings" headers = { "Authorization": "Bearer YOUR_TOKEN", "Content-Type": "application/json" } params = { "date_from": "2024-01-01", "date_to": "2024-01-31" } response = requests.get(url, headers=headers, params=params) embeddings_usage = response.json() print(embeddings_usage) ``` *** ## Get Scraping Usage Get statistics about web scraping operations performed for content extraction. ### Query Parameters Start date for usage data (ISO 8601 format) End date for usage data (ISO 8601 format) ### Response Array of scraping usage statistics The scraping service provider used Type of scraping operation performed Total scraping credits consumed ```bash cURL theme={null} curl --location --request GET 'https://api.plaisolutions.com/usage/scraping?date_from=2024-01-01&date_to=2024-01-31' \ --header 'Authorization: Bearer YOUR_TOKEN' ``` ```javascript JavaScript theme={null} const response = await fetch('https://api.plaisolutions.com/usage/scraping?date_from=2024-01-01&date_to=2024-01-31', { method: 'GET', headers: { 'Authorization': 'Bearer YOUR_TOKEN', 'Content-Type': 'application/json' } }); const scrapingUsage = await response.json(); console.log(scrapingUsage); ``` ```python Python theme={null} import requests url = "https://api.plaisolutions.com/usage/scraping" headers = { "Authorization": "Bearer YOUR_TOKEN", "Content-Type": "application/json" } params = { "date_from": "2024-01-01", "date_to": "2024-01-31" } response = requests.get(url, headers=headers, params=params) scraping_usage = response.json() print(scraping_usage) ``` *** ## Get Web Search Usage Get statistics about web search operations performed through integrated search tools. ### Query Parameters Start date for usage data (ISO 8601 format) End date for usage data (ISO 8601 format) ### Response Array of web search usage statistics The search service provider (e.g., "perplexity", "google") The search model or service tier used Input tokens used for search queries Output tokens generated from search results Total number of search queries performed ```bash cURL theme={null} curl --location --request GET 'https://api.plaisolutions.com/usage/web-search?date_from=2024-01-01&date_to=2024-01-31' \ --header 'Authorization: Bearer YOUR_TOKEN' ``` ```javascript JavaScript theme={null} const response = await fetch('https://api.plaisolutions.com/usage/web-search?date_from=2024-01-01&date_to=2024-01-31', { method: 'GET', headers: { 'Authorization': 'Bearer YOUR_TOKEN', 'Content-Type': 'application/json' } }); const webSearchUsage = await response.json(); console.log(webSearchUsage); ``` ```python Python theme={null} import requests url = "https://api.plaisolutions.com/usage/web-search" headers = { "Authorization": "Bearer YOUR_TOKEN", "Content-Type": "application/json" } params = { "date_from": "2024-01-01", "date_to": "2024-01-31" } response = requests.get(url, headers=headers, params=params) web_search_usage = response.json() print(web_search_usage) ``` *** ## Get Speech-to-Text Usage Get statistics about audio transcription operations performed using speech-to-text services. ### Query Parameters Start date for usage data (ISO 8601 format) End date for usage data (ISO 8601 format) ### Response Array of speech-to-text usage statistics The service provider used for transcription The transcription model used (e.g., "whisper-1") Total audio bytes processed for transcription ```bash cURL theme={null} curl --location --request GET 'https://api.plaisolutions.com/usage/speech-to-text?date_from=2024-01-01&date_to=2024-01-31' \ --header 'Authorization: Bearer YOUR_TOKEN' ``` ```javascript JavaScript theme={null} const response = await fetch('https://api.plaisolutions.com/usage/speech-to-text?date_from=2024-01-01&date_to=2024-01-31', { method: 'GET', headers: { 'Authorization': 'Bearer YOUR_TOKEN', 'Content-Type': 'application/json' } }); const speechToTextUsage = await response.json(); console.log(speechToTextUsage); ``` ```python Python theme={null} import requests url = "https://api.plaisolutions.com/usage/speech-to-text" headers = { "Authorization": "Bearer YOUR_TOKEN", "Content-Type": "application/json" } params = { "date_from": "2024-01-01", "date_to": "2024-01-31" } response = requests.get(url, headers=headers, params=params) speech_to_text_usage = response.json() print(speech_to_text_usage) ``` *** ## Register Speech-to-Text Usage Register speech-to-text usage in the system. This endpoint is typically used by internal services to track transcription usage. This endpoint requires API key authentication with `Users-Management-Key` header and is intended for administrative use. ### Request Body The transcription model used (e.g., "OPENAI\_WHISPER") The service provider used for transcription Number of audio bytes processed ID of the project to attribute the usage to ### Response Returns a confirmation of usage registration. ```bash cURL theme={null} curl --location --request POST 'https://api.plaisolutions.com/usage/whisper' \ --header 'Users-Management-Key: YOUR_MANAGEMENT_KEY' \ --header 'Content-Type: application/json' \ --data-raw '{ "llm_model": "OPENAI_WHISPER", "llm_provider": "OPENAI", "bytes": 1048576, "project_id": "project_123" }' ``` ```javascript JavaScript theme={null} const response = await fetch('https://api.plaisolutions.com/usage/whisper', { method: 'POST', headers: { 'Users-Management-Key': 'YOUR_MANAGEMENT_KEY', 'Content-Type': 'application/json' }, body: JSON.stringify({ llm_model: "OPENAI_WHISPER", llm_provider: "OPENAI", bytes: 1048576, project_id: "project_123" }) }); const result = await response.json(); console.log(result); ``` ```python Python theme={null} import requests url = "https://api.plaisolutions.com/usage/whisper" headers = { "Users-Management-Key": "YOUR_MANAGEMENT_KEY", "Content-Type": "application/json" } data = { "llm_model": "OPENAI_WHISPER", "llm_provider": "OPENAI", "bytes": 1048576, "project_id": "project_123" } response = requests.post(url, headers=headers, json=data) result = response.json() print(result) ``` *** ## Usage Analytics Best Practices Check usage statistics regularly to track consumption patterns and optimize costs Implement usage monitoring and alerts to prevent unexpected cost overruns Use time-based filtering to identify usage trends and plan capacity accordingly Use detailed usage data to identify inefficient operations and optimize them Leverage historical usage data for accurate budget forecasting and planning ### Understanding Usage Metrics Monitor prompt and completion tokens to understand LLM costs and optimize prompt engineering Analyze request timing and frequency to identify peak usage periods and scale accordingly Track usage across different services to identify your primary cost drivers Monitor conversation patterns to understand user engagement and agent effectiveness ### Cost Optimization Tips **Token Management:** * Use shorter prompts when possible to reduce input token costs * Implement response length limits to control completion token usage * Consider using smaller models for simple tasks **Batch Processing:** * Use batch APIs for non-real-time operations to reduce costs by up to 50% * Aggregate similar requests to minimize API overhead **Caching Strategy:** * Implement response caching for frequently asked questions * Use embeddings caching to avoid re-generating vectors for existing content ### Usage Reporting Dashboard Create comprehensive usage dashboards using the analytics data: ```javascript theme={null} // Example: Building a usage dashboard async function fetchAllUsageData(dateFrom, dateTo) { const endpoints = [ 'requests', 'threads', 'llm', 'embeddings', 'scraping', 'web-search', 'speech-to-text' ]; const usageData = {}; for (const endpoint of endpoints) { const response = await fetch(`/usage/${endpoint}?date_from=${dateFrom}&date_to=${dateTo}`, { headers: { 'Authorization': 'Bearer YOUR_TOKEN' } }); usageData[endpoint] = await response.json(); } return usageData; } ``` ### Billing Integration Usage statistics directly correlate with billing charges: * **LLM Usage**: Charged per token (input + output) * **Embeddings**: Charged per token processed * **Scraping**: Charged per credit consumed * **Web Search**: Charged per query + token usage * **Speech-to-Text**: Charged per audio minute/byte * **Reranking**: Charged per search unit **Rate Limiting Considerations:** * High usage may trigger rate limiting * Monitor request patterns to stay within limits * Implement exponential backoff for retry logic * Consider upgrading to higher tier plans for increased limits