NeuralFlow

The Problem

Enterprises sit on vast internal knowledge bases — documentation, Confluence wikis, Notion pages, Slack archives — but employees can’t efficiently search across them. Traditional keyword search fails at semantic queries, and LLMs hallucinate without grounding.

The Solution

NeuralFlow implements a production-grade RAG (Retrieval-Augmented Generation) pipeline. Documents are chunked, embedded using OpenAI’s text-embedding-3-small, and stored in Pinecone. At query time, the top-k semantically similar chunks are retrieved and passed as context to GPT-4, dramatically reducing hallucinations.

A Redis semantic cache stores recent query embeddings — if a new query is within a cosine similarity threshold of a cached query, the cached response is returned instantly, achieving sub-10ms response times for warm queries.

Architecture

The system uses a FastAPI backend with async processing, a Celery worker pool for document ingestion, and a React frontend dashboard for monitoring pipeline health and query analytics.