Technical Deep Dive: Building an AI-Powered Real Time Root Cause Analysis System

Core Components and Implementation

1. AI and Context Management

• Retrieval-Augmented Generation (RAG): The system uses a RAG pipeline to provide dynamic, context-aware responses. Instead of relying only on the LLM's pre-trained knowledge, the RAG model retrieves relevant, up-to-date information from our data sources and uses this context to generate a more accurate analysis.
• Conversational Context: We used LangChain's BufferMemory to manage conversation history. This was configured with a 2000-token sliding window, allowing the system to maintain context across multiple user interactions within a session for coherent multi-turn dialogue.

2. Data Processing and Performance

• Asynchronous Data Processing with Web Workers: To keep the UI responsive during intensive data transformations, we delegated these tasks to a Web Worker. The formatProductData.worker.js script handles complex client-side calculations, such as traffic metrics, conversion analysis, and campaign-level aggregations, running them in a separate background thread.
• Data Chunking and Filtering: To efficiently manage large datasets, the pipeline automatically chunks incoming data. Targeted filters are applied based on the query context and data type (e.g., traffic, revenue, impressions), ensuring only relevant data segments are processed.
• Real-time Token Streaming: For quicker feedback during AI generation, we implemented token-level streaming using LangChain’s callback architecture. This displays the response word-by-word as generated by the LLM, rather than waiting for the full response to finish.

3. Frontend Development

• Rich Text Rendering: The chat interface uses TipTap, a headless editor framework, to render rich text and parse Markdown. This supports various formats, including bold, lists, tables, links, and code blocks, directly in the chat output.

4. Backend and System Stability

• Rate Limiting and Request Queuing: To maintain stability and manage costs, the backend implements a rate limiter that allows a 1-second interval between requests to the LLM API. A queuing system handles concurrent requests, preventing API overloads during peak times.
• Caching Strategy: A custom in-memory caching system stores the results of completed analyses. This persistence avoids redundant processing for repeated queries, improving response times significantly.
• Error Handling: We implemented fail-safe layers across all asynchronous operations. In case of an error, the system provides human-readable messages and graceful fallbacks to ensure a consistent user experience.

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Technical Challenges and Solutions

Challenge	Solution
Real-time Processing Latency	We implemented a combination of data chunking, aggressive caching of analysis results, and token-level streaming to the UI.
Maintaining Context	We used LangChain's BufferMemory with an optimized token window for seamless multi-turn interactions.
API Rate Throttling	We developed a queue-based request management system with a fixed-interval limiter to stabilize performance.
Data Accuracy & Hallucination	We employed a RAG architecture with validated prompts and result-checking layers to ground LLM responses in factual data.