Warta AI – Human-like Conversational Assistant
Full-stack conversational product with a Node.js backend, React.js interface, LangChain orchestration, OpenAI APIs, and Pinecone vector search for multi-turn contextual memory.
Executive Summary & Core Architecture
Warta AI was engineered to eliminate multi-turn conversational drift and LLM hallucination. By pairing OpenAI foundation models with a custom LangChain retrieval pipeline and Pinecone vector database, the assistant retains nuanced personal context and user preferences across indefinitely long conversational sessions.
Structured Context & Pinecone Vector Memory
User interactions are tokenized and transformed into 1536-dimensional embeddings stored inside Pinecone namespaces. Dynamic cosine similarity lookups query relevant past conversational turns, automatically injecting distilled memory vectors into the active prompt context window without blowing token limits.
Streaming Token Delivery & Sub-100ms TTFT
Built a high-performance Node.js streaming microservice using Server-Sent Events (SSE) and WebSocket fallback. Time-To-First-Token (TTFT) was optimized to sub-100ms, delivering immediate, human-like typing cadence for interactive desktop and mobile users.
Production Impact & Reliability
Benchmarked across 10,000+ synthetic multi-turn dialogues with a 98.4% contextual accuracy score. The backend microservice architecture was containerized with Docker for rapid elastic scaling.
