RAG Knowledge Assistant
A custom RAG pipeline for answering company-specific questions.
# Description
Built a Retrieval-Augmented Generation (RAG) system for Opryon Labs that enables employees to quickly find answers to company-specific questions from internal documentation and knowledge bases.
# Tech Stack
- Python for data processing and orchestration
- LangChain for RAG pipeline implementation
- OpenAI embeddings and GPT-4 for generation
- Pinecone vector database for similarity search
- FastAPI for performant REST API endpoints
# Problem
Employees spent significant time searching through scattered documentation, Slack threads, and wikis to find answers to common questions. This knowledge fragmentation reduced productivity and slowed onboarding.
# Solution
Developed a custom RAG pipeline using LangChain and OpenAI that ingests company documentation, creates vector embeddings, and stores them in Pinecone. Built a FastAPI backend that retrieves relevant context and generates accurate answers with source citations.
# Results
Reduced time spent searching for information by 60%. Improved onboarding speed for new team members. Created a centralized knowledge system that stays up-to-date with company documentation.