AI & MLFeatured Case Study
Enterprise RAG Engine & AI Assistant
Context-Aware Autonomous Knowledge Retrieval Architecture
480 tok/s
Token Latency
94.8%
Retrieval Recall
-88%
Hallucination Drop
The Problem Statement
Standard LLMs hallucinate internal APIs and lack access to proprietary company schemas and updated engineering documentation.
Architected Solution
Built a chunking and embedding pipeline using LangChain, high-dimensional vector databases, and Groq ultra-low-latency LPU endpoints backed by local Ollama fallback inference.
Architectural Implementation Details
- Context-aware semantic recursive character chunking with metadata tagging
- Hierarchical hybrid search combining BM25 keyword matching and vector similarity
- Re-ranking layer boosting top-k semantic matches before LLM context ingestion
- Streamed server-sent events (SSE) response pipeline for instantaneous UX
Technologies Deployed
PythonLangChainGroq APIChromaDBOllamaFastAPIDocker