TelemetryLLM

# TelemetryLLM SDK and tools for Langchain and MCP to fetch telemetry and insights into your application. Support for Grafana, PostHog, Azure Data Explorer, Prometheus, Umami, etc. ## Objectives - **Data Integration:** Connect seamlessly with popular telemetry and analytics services. - **Natural Language Generation:** Convert raw telemetry data into concise, actionable summaries. - **LLM Compatibility:** Optimize output for processing by LLMs in platforms like LangChain and MCP servers. - **Scalability:** Handle large data volumes with efficient data processing. - **Real-time Telemetry Logs:** Continuously fetch and update telemetry data to provide real-time insights. ## Key Features 1. **Multi-Service Data Connectors:** - Integrations with Vercel Analytics, Google Analytics, PostHog, Prometheus, Grafana, and Kusto. - Configurable authentication via API keys or OAuth. 2. **Data Fetching & Preprocessing:** - Standardized data extraction methods. - Preprocessing pipelines for cleaning and normalizing data. - **Real-time Data Sync:** Automatic updates with the latest telemetry logs for real-time monitoring. 3. **Summarization Engine:** - Natural Language Generation (NLG) models for human-readable summaries. - Customizable summary formats (technical, executive summaries, insights). 4. **LLM-Ready Output:** - Integration-ready summaries for frameworks like LangChain and MCP servers. - JSON or text formats for downstream processing. 5. **Extensibility:** - Modular architecture for easy integration of new telemetry sources. - API hooks for custom data processing and summarization workflows. ## Architecture ### Components - **TelemetryConnector:** Handles data retrieval from telemetry services. - **TelemetrySummarizer:** Converts telemetry data into natural language summaries. - **TelemetrySDK:** Orchestrates data fetching and summarization. - **TelemetryUpdater:** Continuously polls and updates the latest telemetry logs in real-time. ### Workflow 1. **Data Retrieval:** Connect to the selected service using its API. 2. **Preprocessing:** Normalize and clean the data. 3. **Real-time Sync:** Periodic updates to reflect the most recent telemetry data. 4. **Summarization:** Generate human-readable summaries. 5. **Output:** Deliver summaries in formats ready for LLM consumption. ## Integration with LangChain and MCP Servers - **LangChain:** Implement custom data loaders and processors for seamless integration. - **MCP Servers:** Create MCP-compliant servers for secure data exchange and querying. ## Example Use Case ```python sdk = TelemetrySDK(service_name='vercel', api_key='YOUR_API_KEY') summary = sdk.fetch_and_summarize(endpoint='analytics/events', params={'projectId': 'your_project_id'}) print(summary) # Real-time update sdk.start_real_time_updates(interval=60) # Fetches new logs every 60 seconds